diff --git a/examples/run_all.sh b/examples/run_all.sh
index cc40d772656e11259e4aac32b47a2ad74bf4e634..85a1f6dbbad26606a9d78d6671b7126f03013ce0 100755
--- a/examples/run_all.sh
+++ b/examples/run_all.sh
@@ -6,3 +6,10 @@ for file in `ls *.py`; do
python $file
done
+for file in `ls svm/*.py`; do
+ python $file
+done
+
+for file in `ls em/*.py`; do
+ python $file
+done
diff --git a/examples/svm/plot_svm_hyperplane.py b/examples/svm/plot_svm_hyperplane.py
index f2e930552ffb6c30658063d5a1a2b5d7881d8253..18858b73c34f239f6aa56e2ab4302fef4ac6bec6 100644
--- a/examples/svm/plot_svm_hyperplane.py
+++ b/examples/svm/plot_svm_hyperplane.py
@@ -22,7 +22,7 @@ clf.fit(X, Y)
w = np.dot(clf.dual_coef_[0], clf.support_)
a = -w[0]/w[1]
xx = np.linspace(-5, 5)
-yy = a*xx + (clf.rho_[0])/w[1]
+yy = a*xx - (clf.intercept_[0])/w[1]
# plot the parallels to the separating hyperplane that pass through the
# support vectors
diff --git a/scikits/learn/src/libsvm.c b/scikits/learn/src/libsvm.c
index 53e46bec9c184d4e3e7e449c259967e4273028e4..17fb936b428d98ad06b15e7e9cef4eb9295b40ff 100644
--- a/scikits/learn/src/libsvm.c
+++ b/scikits/learn/src/libsvm.c
@@ -1,11 +1,11 @@
-/* Generated by Cython 0.11.2 on Thu Mar 4 17:07:19 2010 */
+/* Generated by Cython 0.12 on Tue Apr 27 19:58:59 2010 */
#define PY_SSIZE_T_CLEAN
#include "Python.h"
#include "structmember.h"
#ifndef Py_PYTHON_H
#error Python headers needed to compile C extensions, please install development version of Python.
-#endif
+#else
#ifndef PY_LONG_LONG
#define PY_LONG_LONG LONG_LONG
#endif
@@ -15,6 +15,7 @@
#if PY_VERSION_HEX < 0x02040000
#define METH_COEXIST 0
#define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type)
+ #define PyDict_Contains(d,o) PySequence_Contains(d,o)
#endif
#if PY_VERSION_HEX < 0x02050000
typedef int Py_ssize_t;
@@ -73,8 +74,13 @@
#endif
#if PY_MAJOR_VERSION >= 3
#define PyBaseString_Type PyUnicode_Type
- #define PyString_Type PyBytes_Type
- #define PyString_CheckExact PyBytes_CheckExact
+ #define PyString_Type PyUnicode_Type
+ #define PyString_CheckExact PyUnicode_CheckExact
+#else
+ #define PyBytes_Type PyString_Type
+ #define PyBytes_CheckExact PyString_CheckExact
+#endif
+#if PY_MAJOR_VERSION >= 3
#define PyInt_Type PyLong_Type
#define PyInt_Check(op) PyLong_Check(op)
#define PyInt_CheckExact(op) PyLong_CheckExact(op)
@@ -89,9 +95,10 @@
#define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
#define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
#define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
+ #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y)
#else
#define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y)
- #define PyBytes_Type PyString_Type
+ #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y)
#endif
#if PY_MAJOR_VERSION >= 3
#define PyMethod_New(func, self, klass) PyInstanceMethod_New(func)
@@ -133,11 +140,11 @@
#include <math.h>
#define __PYX_HAVE_API__libsvm
#include "stdlib.h"
+#include "stdio.h"
#include "numpy/arrayobject.h"
+#include "numpy/ufuncobject.h"
#include "svm.h"
#include "libsvm_helper.c"
-#define __PYX_USE_C99_COMPLEX defined(_Complex_I)
-
#ifdef __GNUC__
#define INLINE __inline__
@@ -147,11 +154,7 @@
#define INLINE
#endif
-typedef struct {PyObject **p; char *s; long n; char is_unicode; char intern; char is_identifier;} __Pyx_StringTabEntry; /*proto*/
-
-
-
-static int __pyx_skip_dispatch = 0;
+typedef struct {PyObject **p; char *s; const long n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
/* Type Conversion Predeclarations */
@@ -166,6 +169,9 @@ static int __pyx_skip_dispatch = 0;
#define __Pyx_PyBytes_AsString PyBytes_AsString
#endif
+#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s)
+#define __Pyx_PyBytes_AsUString(s) ((unsigned char*) __Pyx_PyBytes_AsString(s))
+
#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
static INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
@@ -185,6 +191,40 @@ static INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
#endif
#endif
+
+#if !defined(T_ULONGLONG)
+#define __Pyx_T_UNSIGNED_INT(x) \
+ ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \
+ ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \
+ ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \
+ ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : -1))))
+#else
+#define __Pyx_T_UNSIGNED_INT(x) \
+ ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \
+ ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \
+ ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \
+ ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : \
+ ((sizeof(x) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1)))))
+#endif
+#if !defined(T_LONGLONG)
+#define __Pyx_T_SIGNED_INT(x) \
+ ((sizeof(x) == sizeof(char)) ? T_BYTE : \
+ ((sizeof(x) == sizeof(short)) ? T_SHORT : \
+ ((sizeof(x) == sizeof(int)) ? T_INT : \
+ ((sizeof(x) == sizeof(long)) ? T_LONG : -1))))
+#else
+#define __Pyx_T_SIGNED_INT(x) \
+ ((sizeof(x) == sizeof(char)) ? T_BYTE : \
+ ((sizeof(x) == sizeof(short)) ? T_SHORT : \
+ ((sizeof(x) == sizeof(int)) ? T_INT : \
+ ((sizeof(x) == sizeof(long)) ? T_LONG : \
+ ((sizeof(x) == sizeof(PY_LONG_LONG)) ? T_LONGLONG : -1)))))
+#endif
+
+#define __Pyx_T_FLOATING(x) \
+ ((sizeof(x) == sizeof(float)) ? T_FLOAT : \
+ ((sizeof(x) == sizeof(double)) ? T_DOUBLE : -1))
+
#if !defined(T_SIZET)
#if !defined(T_ULONGLONG)
#define T_SIZET \
@@ -222,44 +262,145 @@ static INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
static PyObject *__pyx_m;
static PyObject *__pyx_b;
static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
static int __pyx_lineno;
static int __pyx_clineno = 0;
static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;
static const char **__pyx_f;
-static char __pyx_mdoc[] = "\nBinding for libsvm[1]\n---------------------\nWe do not use the binding that ships with libsvm because we need to\naccess svm_model.sv_coeff (and other fields), but libsvm does not\nprovide an accessor. Our solution is to export svm_model and access it\nmanually, this is done un function see svm_train_wrap.\n\nlibsvm uses an sparse representation for the training vectors. In\nmethod dense_to_sparse we translate a dense matrix representation as\nthose produced by NumPy to a sparse representation that libsvm can\nunderstand.\n\nWe define arrays to be the same type as those in libsvm, usually of \ntype C double and C int.\n\nLow-level memory management is done in libsvm_helper.c. If we happen\nto run out of memory a MemoryError will be raised. In practice this is\nnot very helpful since hight changes are malloc fails inside svm.cpp,\nwhere no sort of memory checks are done.\n\nThese are low-level routines, not meant to be used directly. See\nscikits.learn.svm for a higher-level API.\n\n[1] http://www.csie.ntu.edu.tw/~cjlin/libsvm/\n\nNotes\n-----\nMaybe we could speed it a bit further by decorating functions with\n@cython.boundscheck(False), but probably it is not worth since all\nwork is done in lisvm_helper.c\nAlso, the signature mode='c' is somewhat superficial, since we already\ncheck that arrays are C-contiguous in svm.py\n\nAuthors\n-------\n2010: Fabian Pedregosa <fabian.pedregosa@inria.fr>\n Gael Varoquaux <gael.varoquaux@normalesup.org>\n";
+#if !defined(CYTHON_CCOMPLEX)
+ #if defined(__cplusplus)
+ #define CYTHON_CCOMPLEX 1
+ #elif defined(_Complex_I)
+ #define CYTHON_CCOMPLEX 1
+ #else
+ #define CYTHON_CCOMPLEX 0
+ #endif
+#endif
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ #include <complex>
+ #else
+ #include <complex.h>
+ #endif
+#endif
-#ifdef CYTHON_REFNANNY
-typedef struct {
- void (*INCREF)(void*, PyObject*, int);
- void (*DECREF)(void*, PyObject*, int);
- void (*GOTREF)(void*, PyObject*, int);
- void (*GIVEREF)(void*, PyObject*, int);
- void* (*NewContext)(const char*, int, const char*);
- void (*FinishContext)(void**);
-} __Pyx_RefnannyAPIStruct;
-static __Pyx_RefnannyAPIStruct *__Pyx_Refnanny = NULL;
-#define __Pyx_ImportRefcountAPI(name) (__Pyx_RefnannyAPIStruct *) PyCObject_Import((char *)name, (char *)"RefnannyAPI")
-#define __Pyx_INCREF(r) __Pyx_Refnanny->INCREF(__pyx_refchk, (PyObject *)(r), __LINE__)
-#define __Pyx_DECREF(r) __Pyx_Refnanny->DECREF(__pyx_refchk, (PyObject *)(r), __LINE__)
-#define __Pyx_GOTREF(r) __Pyx_Refnanny->GOTREF(__pyx_refchk, (PyObject *)(r), __LINE__)
-#define __Pyx_GIVEREF(r) __Pyx_Refnanny->GIVEREF(__pyx_refchk, (PyObject *)(r), __LINE__)
-#define __Pyx_XDECREF(r) if((r) == NULL) ; else __Pyx_DECREF(r)
-#define __Pyx_SetupRefcountContext(name) void* __pyx_refchk = __Pyx_Refnanny->NewContext((name), __LINE__, __FILE__)
-#define __Pyx_FinishRefcountContext() __Pyx_Refnanny->FinishContext(&__pyx_refchk)
+typedef npy_int8 __pyx_t_5numpy_int8_t;
+
+typedef npy_int16 __pyx_t_5numpy_int16_t;
+
+typedef npy_int32 __pyx_t_5numpy_int32_t;
+
+typedef npy_int64 __pyx_t_5numpy_int64_t;
+
+typedef npy_uint8 __pyx_t_5numpy_uint8_t;
+
+typedef npy_uint16 __pyx_t_5numpy_uint16_t;
+
+typedef npy_uint32 __pyx_t_5numpy_uint32_t;
+
+typedef npy_uint64 __pyx_t_5numpy_uint64_t;
+
+typedef npy_float32 __pyx_t_5numpy_float32_t;
+
+typedef npy_float64 __pyx_t_5numpy_float64_t;
+
+typedef npy_long __pyx_t_5numpy_int_t;
+
+typedef npy_longlong __pyx_t_5numpy_long_t;
+
+typedef npy_intp __pyx_t_5numpy_intp_t;
+
+typedef npy_uintp __pyx_t_5numpy_uintp_t;
+
+typedef npy_ulong __pyx_t_5numpy_uint_t;
+
+typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
+
+typedef npy_double __pyx_t_5numpy_float_t;
+
+typedef npy_double __pyx_t_5numpy_double_t;
+
+typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ typedef ::std::complex< float > __pyx_t_float_complex;
+ #else
+ typedef float _Complex __pyx_t_float_complex;
+ #endif
+#else
+ typedef struct { float real, imag; } __pyx_t_float_complex;
+#endif
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ typedef ::std::complex< double > __pyx_t_double_complex;
+ #else
+ typedef double _Complex __pyx_t_double_complex;
+ #endif
+#else
+ typedef struct { double real, imag; } __pyx_t_double_complex;
+#endif
+
+/* Type declarations */
+
+typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
+
+typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
+
+typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
+
+typedef npy_cdouble __pyx_t_5numpy_complex_t;
+
+#ifndef CYTHON_REFNANNY
+ #define CYTHON_REFNANNY 0
+#endif
+
+#if CYTHON_REFNANNY
+ typedef struct {
+ void (*INCREF)(void*, PyObject*, int);
+ void (*DECREF)(void*, PyObject*, int);
+ void (*GOTREF)(void*, PyObject*, int);
+ void (*GIVEREF)(void*, PyObject*, int);
+ void* (*SetupContext)(const char*, int, const char*);
+ void (*FinishContext)(void**);
+ } __Pyx_RefNannyAPIStruct;
+ static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
+ static __Pyx_RefNannyAPIStruct * __Pyx_RefNannyImportAPI(const char *modname) {
+ PyObject *m = NULL, *p = NULL;
+ void *r = NULL;
+ m = PyImport_ImportModule((char *)modname);
+ if (!m) goto end;
+ p = PyObject_GetAttrString(m, (char *)"RefNannyAPI");
+ if (!p) goto end;
+ r = PyLong_AsVoidPtr(p);
+ end:
+ Py_XDECREF(p);
+ Py_XDECREF(m);
+ return (__Pyx_RefNannyAPIStruct *)r;
+ }
+ #define __Pyx_RefNannySetupContext(name) void *__pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
+ #define __Pyx_RefNannyFinishContext() __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
+ #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+ #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+ #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+ #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+ #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r);} } while(0)
#else
-#define __Pyx_INCREF(r) Py_INCREF(r)
-#define __Pyx_DECREF(r) Py_DECREF(r)
-#define __Pyx_GOTREF(r)
-#define __Pyx_GIVEREF(r)
-#define __Pyx_XDECREF(r) Py_XDECREF(r)
-#define __Pyx_SetupRefcountContext(name)
-#define __Pyx_FinishRefcountContext()
+ #define __Pyx_RefNannySetupContext(name)
+ #define __Pyx_RefNannyFinishContext()
+ #define __Pyx_INCREF(r) Py_INCREF(r)
+ #define __Pyx_DECREF(r) Py_DECREF(r)
+ #define __Pyx_GOTREF(r)
+ #define __Pyx_GIVEREF(r)
+ #define __Pyx_XDECREF(r) Py_XDECREF(r)
#endif /* CYTHON_REFNANNY */
-#define __Pyx_XGIVEREF(r) if((r) == NULL) ; else __Pyx_GIVEREF(r)
-#define __Pyx_XGOTREF(r) if((r) == NULL) ; else __Pyx_GOTREF(r)
+#define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);} } while(0)
+#define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r);} } while(0)
static void __Pyx_RaiseDoubleKeywordsError(
const char* func_name, PyObject* kw_name); /*proto*/
@@ -294,86 +435,25 @@ typedef struct {
static INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
static int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
-static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
+static INLINE long __Pyx_div_long(long, long); /* proto */
+
+static INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
static void __Pyx_RaiseBufferFallbackError(void); /*proto*/
static INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
static INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+static INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-static INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
- PyObject *r;
- if (!j) return NULL;
- r = PyObject_GetItem(o, j);
- Py_DECREF(j);
- return r;
-}
-
-
-#define __Pyx_GetItemInt_List(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \
- __Pyx_GetItemInt_List_Fast(o, i, size <= sizeof(long)) : \
- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
-
-static INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int fits_long) {
- if (likely(o != Py_None)) {
- if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
- PyObject *r = PyList_GET_ITEM(o, i);
- Py_INCREF(r);
- return r;
- }
- else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) {
- PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i);
- Py_INCREF(r);
- return r;
- }
- }
- return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i));
-}
-
-#define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \
- __Pyx_GetItemInt_Tuple_Fast(o, i, size <= sizeof(long)) : \
- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
-
-static INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int fits_long) {
- if (likely(o != Py_None)) {
- if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
- PyObject *r = PyTuple_GET_ITEM(o, i);
- Py_INCREF(r);
- return r;
- }
- else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) {
- PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i);
- Py_INCREF(r);
- return r;
- }
- }
- return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i));
-}
+static INLINE void __Pyx_RaiseTooManyValuesError(void);
+static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/
+static int __Pyx_EndUnpack(PyObject *); /*proto*/
-#define __Pyx_GetItemInt(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \
- __Pyx_GetItemInt_Fast(o, i, size <= sizeof(long)) : \
- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
+static INLINE void __Pyx_RaiseNoneNotIterableError(void);
-static INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int fits_long) {
- PyObject *r;
- if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
- r = PyList_GET_ITEM(o, i);
- Py_INCREF(r);
- }
- else if (PyTuple_CheckExact(o) && ((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
- r = PyTuple_GET_ITEM(o, i);
- Py_INCREF(r);
- }
- else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0))) {
- r = PySequence_GetItem(o, i);
- }
- else {
- r = __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i));
- }
- return r;
-}
+static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index); /*proto*/
static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
const char *name, int exact); /*proto*/
@@ -394,221 +474,104 @@ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-#if __PYX_USE_C99_COMPLEX
- #define __Pyx_REAL_PART(z) __real__(z)
- #define __Pyx_IMAG_PART(z) __imag__(z)
+static INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp);
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ #define __Pyx_CREAL(z) ((z).real())
+ #define __Pyx_CIMAG(z) ((z).imag())
+ #else
+ #define __Pyx_CREAL(z) (__real__(z))
+ #define __Pyx_CIMAG(z) (__imag__(z))
+ #endif
#else
- #define __Pyx_REAL_PART(z) ((z).real)
- #define __Pyx_IMAG_PART(z) ((z).imag)
+ #define __Pyx_CREAL(z) ((z).real)
+ #define __Pyx_CIMAG(z) ((z).imag)
#endif
-#define __pyx_PyObject_from_complex(z) PyComplex_FromDoubles((double)__Pyx_REAL_PART(z), (double)__Pyx_IMAG_PART(z))
-
-#if __PYX_USE_C99_COMPLEX
-
- typedef float _Complex __pyx_t_float_complex;
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
- return x + y*(__pyx_t_float_complex)_Complex_I;
- }
-
- #define __pyx_t_float_complex_is_zero(a) ((a) == 0)
- #define __pyx_t_float_complex_eq(a, b) ((a) == (b))
- #define __pyx_t_float_complex_add(a, b) ((a)+(b))
- #define __pyx_t_float_complex_sub(a, b) ((a)-(b))
- #define __pyx_t_float_complex_mul(a, b) ((a)*(b))
- #define __pyx_t_float_complex_div(a, b) ((a)/(b))
- #define __pyx_t_float_complex_neg(a) (-(a))
-
+#if defined(_WIN32) && defined(__cplusplus) && CYTHON_CCOMPLEX
+ #define __Pyx_SET_CREAL(z,x) ((z).real(x))
+ #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
#else
-
- typedef struct { float real, imag; } __pyx_t_float_complex;
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
- __pyx_t_float_complex c; c.real = x; c.imag = y; return c;
- }
-
- static INLINE int __pyx_t_float_complex_is_zero(__pyx_t_float_complex a) {
- return (a.real == 0) & (a.imag == 0);
- }
-
- static INLINE int __pyx_t_float_complex_eq(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- return (a.real == b.real) & (a.imag == b.imag);
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_add(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_sub(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_mul(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_div(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- float denom = b.real*b.real + b.imag*b.imag;
- z.real = (a.real * b.real + a.imag * b.imag) / denom;
- z.imag = (a.imag * b.real - a.real * b.imag) / denom;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_neg(__pyx_t_float_complex a) {
- __pyx_t_float_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
-
+ #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
+ #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
#endif
-#if __PYX_USE_C99_COMPLEX
-
- typedef double _Complex __pyx_t_double_complex;
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
- return x + y*(__pyx_t_double_complex)_Complex_I;
- }
-
- #define __pyx_t_double_complex_is_zero(a) ((a) == 0)
- #define __pyx_t_double_complex_eq(a, b) ((a) == (b))
- #define __pyx_t_double_complex_add(a, b) ((a)+(b))
- #define __pyx_t_double_complex_sub(a, b) ((a)-(b))
- #define __pyx_t_double_complex_mul(a, b) ((a)*(b))
- #define __pyx_t_double_complex_div(a, b) ((a)/(b))
- #define __pyx_t_double_complex_neg(a) (-(a))
-
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
+ #else
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
+ #endif
#else
-
- typedef struct { double real, imag; } __pyx_t_double_complex;
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
- __pyx_t_double_complex c; c.real = x; c.imag = y; return c;
- }
-
- static INLINE int __pyx_t_double_complex_is_zero(__pyx_t_double_complex a) {
- return (a.real == 0) & (a.imag == 0);
- }
-
- static INLINE int __pyx_t_double_complex_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- return (a.real == b.real) & (a.imag == b.imag);
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_add(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_sub(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_mul(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_div(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- double denom = b.real*b.real + b.imag*b.imag;
- z.real = (a.real * b.real + a.imag * b.imag) / denom;
- z.imag = (a.imag * b.real - a.real * b.imag) / denom;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_neg(__pyx_t_double_complex a) {
- __pyx_t_double_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
-
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
#endif
-#if __PYX_USE_C99_COMPLEX
-
- typedef long double _Complex __pyx_t_long__double_complex;
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_from_parts(long double x, long double y) {
- return x + y*(__pyx_t_long__double_complex)_Complex_I;
- }
-
- #define __pyx_t_long__double_complex_is_zero(a) ((a) == 0)
- #define __pyx_t_long__double_complex_eq(a, b) ((a) == (b))
- #define __pyx_t_long__double_complex_add(a, b) ((a)+(b))
- #define __pyx_t_long__double_complex_sub(a, b) ((a)-(b))
- #define __pyx_t_long__double_complex_mul(a, b) ((a)*(b))
- #define __pyx_t_long__double_complex_div(a, b) ((a)/(b))
- #define __pyx_t_long__double_complex_neg(a) (-(a))
-
+#if CYTHON_CCOMPLEX
+ #define __Pyx_c_eqf(a, b) ((a)==(b))
+ #define __Pyx_c_sumf(a, b) ((a)+(b))
+ #define __Pyx_c_difff(a, b) ((a)-(b))
+ #define __Pyx_c_prodf(a, b) ((a)*(b))
+ #define __Pyx_c_quotf(a, b) ((a)/(b))
+ #define __Pyx_c_negf(a) (-(a))
+ #ifdef __cplusplus
+ #define __Pyx_c_is_zerof(z) ((z)==(float)0)
+ #define __Pyx_c_conjf(z) (::std::conj(z))
+ /*#define __Pyx_c_absf(z) (::std::abs(z))*/
+ #else
+ #define __Pyx_c_is_zerof(z) ((z)==0)
+ #define __Pyx_c_conjf(z) (conjf(z))
+ /*#define __Pyx_c_absf(z) (cabsf(z))*/
+ #endif
#else
+ static INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex);
+ static INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex);
+ /*static INLINE float __Pyx_c_absf(__pyx_t_float_complex);*/
+#endif
- typedef struct { long double real, imag; } __pyx_t_long__double_complex;
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_from_parts(long double x, long double y) {
- __pyx_t_long__double_complex c; c.real = x; c.imag = y; return c;
- }
-
- static INLINE int __pyx_t_long__double_complex_is_zero(__pyx_t_long__double_complex a) {
- return (a.real == 0) & (a.imag == 0);
- }
-
- static INLINE int __pyx_t_long__double_complex_eq(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- return (a.real == b.real) & (a.imag == b.imag);
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_add(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_sub(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_mul(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_div(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- long double denom = b.real*b.real + b.imag*b.imag;
- z.real = (a.real * b.real + a.imag * b.imag) / denom;
- z.imag = (a.imag * b.real - a.real * b.imag) / denom;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_neg(__pyx_t_long__double_complex a) {
- __pyx_t_long__double_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
-
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
+ #else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
+ #endif
+#else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
#endif
-static INLINE int __Pyx_StrEq(const char *, const char *); /*proto*/
+#if CYTHON_CCOMPLEX
+ #define __Pyx_c_eq(a, b) ((a)==(b))
+ #define __Pyx_c_sum(a, b) ((a)+(b))
+ #define __Pyx_c_diff(a, b) ((a)-(b))
+ #define __Pyx_c_prod(a, b) ((a)*(b))
+ #define __Pyx_c_quot(a, b) ((a)/(b))
+ #define __Pyx_c_neg(a) (-(a))
+ #ifdef __cplusplus
+ #define __Pyx_c_is_zero(z) ((z)==(double)0)
+ #define __Pyx_c_conj(z) (::std::conj(z))
+ /*#define __Pyx_c_abs(z) (::std::abs(z))*/
+ #else
+ #define __Pyx_c_is_zero(z) ((z)==0)
+ #define __Pyx_c_conj(z) (conj(z))
+ /*#define __Pyx_c_abs(z) (cabs(z))*/
+ #endif
+#else
+ static INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex);
+ static INLINE int __Pyx_c_is_zero(__pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex);
+ /*static INLINE double __Pyx_c_abs(__pyx_t_double_complex);*/
+#endif
static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *);
@@ -647,65 +610,31 @@ static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
static void __Pyx_AddTraceback(const char *funcname); /*proto*/
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
-
-/* Type declarations */
-
-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-typedef npy_int16 __pyx_t_5numpy_int16_t;
-
-typedef npy_int32 __pyx_t_5numpy_int32_t;
-
-typedef npy_int64 __pyx_t_5numpy_int64_t;
-
-typedef npy_uint8 __pyx_t_5numpy_uint8_t;
-
-typedef npy_uint16 __pyx_t_5numpy_uint16_t;
-
-typedef npy_uint32 __pyx_t_5numpy_uint32_t;
-
-typedef npy_uint64 __pyx_t_5numpy_uint64_t;
-
-typedef npy_float32 __pyx_t_5numpy_float32_t;
-
-typedef npy_float64 __pyx_t_5numpy_float64_t;
-
-typedef npy_complex64 __pyx_t_5numpy_complex64_t;
-
-typedef npy_complex128 __pyx_t_5numpy_complex128_t;
-
-typedef npy_long __pyx_t_5numpy_int_t;
-
-typedef npy_longlong __pyx_t_5numpy_long_t;
-
-typedef npy_ulong __pyx_t_5numpy_uint_t;
-
-typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
-
-typedef npy_double __pyx_t_5numpy_float_t;
-
-typedef npy_double __pyx_t_5numpy_double_t;
-
-typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
-
-typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
-
-typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
-
-typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
-
-typedef npy_cdouble __pyx_t_5numpy_complex_t;
/* Module declarations from python_buffer */
+/* Module declarations from python_ref */
+
/* Module declarations from stdlib */
+/* Module declarations from stdio */
+
/* Module declarations from numpy */
/* Module declarations from numpy */
static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
+static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
+static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *, PyObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *, PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/
static INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
+static INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *); /*proto*/
/* Module declarations from libsvm */
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t = { "numpy.float64_t", NULL, sizeof(__pyx_t_5numpy_float64_t), 'R' };
@@ -715,143 +644,172 @@ static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float_t = { "numpy.float_
int __pyx_module_is_main_libsvm = 0;
/* Implementation of libsvm */
-static char __pyx_k___main__[] = "__main__";
-static PyObject *__pyx_kp___main__;
-static char __pyx_k_X[] = "X";
-static PyObject *__pyx_kp_X;
-static char __pyx_k_Y[] = "Y";
-static PyObject *__pyx_kp_Y;
-static char __pyx_k_svm_type[] = "svm_type";
-static PyObject *__pyx_kp_svm_type;
-static char __pyx_k_kernel_type[] = "kernel_type";
-static PyObject *__pyx_kp_kernel_type;
-static char __pyx_k_degree[] = "degree";
-static PyObject *__pyx_kp_degree;
-static char __pyx_k_gamma[] = "gamma";
-static PyObject *__pyx_kp_gamma;
-static char __pyx_k_coef0[] = "coef0";
-static PyObject *__pyx_kp_coef0;
-static char __pyx_k_eps[] = "eps";
-static PyObject *__pyx_kp_eps;
-static char __pyx_k_C[] = "C";
-static PyObject *__pyx_kp_C;
-static char __pyx_k_nr_weight[] = "nr_weight";
-static PyObject *__pyx_kp_nr_weight;
-static char __pyx_k_weight_label[] = "weight_label";
-static PyObject *__pyx_kp_weight_label;
-static char __pyx_k_weight[] = "weight";
-static PyObject *__pyx_kp_weight;
-static char __pyx_k_nu[] = "nu";
-static PyObject *__pyx_kp_nu;
-static char __pyx_k_cache_size[] = "cache_size";
-static PyObject *__pyx_kp_cache_size;
-static char __pyx_k_p[] = "p";
-static PyObject *__pyx_kp_p;
-static char __pyx_k_shrinking[] = "shrinking";
-static PyObject *__pyx_kp_shrinking;
-static char __pyx_k_probability[] = "probability";
-static PyObject *__pyx_kp_probability;
-static char __pyx_k_T[] = "T";
-static PyObject *__pyx_kp_T;
-static char __pyx_k_SV[] = "SV";
-static PyObject *__pyx_kp_SV;
-static char __pyx_k_sv_coef[] = "sv_coef";
-static PyObject *__pyx_kp_sv_coef;
-static char __pyx_k_rho[] = "rho";
-static PyObject *__pyx_kp_rho;
-static char __pyx_k_nr_class[] = "nr_class";
-static PyObject *__pyx_kp_nr_class;
-static char __pyx_k_nSV[] = "nSV";
-static PyObject *__pyx_kp_nSV;
-static char __pyx_k_label[] = "label";
-static PyObject *__pyx_kp_label;
-static char __pyx_k_probA[] = "probA";
-static PyObject *__pyx_kp_probA;
-static char __pyx_k_probB[] = "probB";
-static PyObject *__pyx_kp_probB;
-static char __pyx_k_numpy[] = "numpy";
-static PyObject *__pyx_kp_numpy;
-static char __pyx_k_np[] = "np";
-static PyObject *__pyx_kp_np;
-static char __pyx_k_MemoryError[] = "MemoryError";
-static PyObject *__pyx_kp_MemoryError;
-static char __pyx_k_ValueError[] = "ValueError";
-static PyObject *__pyx_kp_ValueError;
-static char __pyx_k_empty[] = "empty";
-static PyObject *__pyx_kp_empty;
-static char __pyx_k_zeros[] = "zeros";
-static PyObject *__pyx_kp_zeros;
-static char __pyx_k_dtype[] = "dtype";
-static PyObject *__pyx_kp_dtype;
-static char __pyx_k_66[] = "int32";
-static PyObject *__pyx_kp_66;
-static char __pyx_k_67[] = "float64";
-static PyObject *__pyx_kp_67;
static PyObject *__pyx_builtin_MemoryError;
static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_kp_65;
-static char __pyx_k_65[] = "Seems we've run out of of memory";
-static PyObject *__pyx_kp_68;
-static char __pyx_k_68[] = "We've run out of of memory";
-static PyObject *__pyx_kp_69;
-static char __pyx_k_69[] = "We've run out of of memory";
-static PyObject *__pyx_int_15;
-static char __pyx_k___getbuffer__[] = "__getbuffer__";
-static PyObject *__pyx_kp___getbuffer__;
-static char __pyx_k___releasebuffer__[] = "__releasebuffer__";
-static PyObject *__pyx_kp___releasebuffer__;
-static char __pyx_k_info[] = "info";
-static PyObject *__pyx_kp_info;
-static char __pyx_k_flags[] = "flags";
-static PyObject *__pyx_kp_flags;
-static char __pyx_k_range[] = "range";
-static PyObject *__pyx_kp_range;
-static char __pyx_k_itervalues[] = "itervalues";
-static PyObject *__pyx_kp_itervalues;
-static char __pyx_k_RuntimeError[] = "RuntimeError";
-static PyObject *__pyx_kp_RuntimeError;
-static PyObject *__pyx_kp_35;
-static PyObject *__pyx_kp_36;
-static PyObject *__pyx_kp_39;
-static PyObject *__pyx_kp_57;
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_RuntimeError;
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-static char __pyx_k_36[] = "ndarray is not Fortran contiguous";
-static char __pyx_k_37[] = ">";
-static char __pyx_k_38[] = "<";
-static char __pyx_k_39[] = "Non-native byte order not supported";
-static char __pyx_k_40[] = "b";
-static char __pyx_k_41[] = "B";
-static char __pyx_k_42[] = "h";
-static char __pyx_k_43[] = "H";
-static char __pyx_k_44[] = "i";
-static char __pyx_k_45[] = "I";
-static char __pyx_k_46[] = "l";
-static char __pyx_k_47[] = "L";
-static char __pyx_k_48[] = "q";
-static char __pyx_k_49[] = "Q";
-static char __pyx_k_50[] = "f";
-static char __pyx_k_51[] = "d";
-static char __pyx_k_52[] = "g";
-static char __pyx_k_53[] = "Zf";
-static char __pyx_k_54[] = "Zd";
-static char __pyx_k_55[] = "Zg";
-static char __pyx_k_56[] = "O";
-static char __pyx_k_57[] = "unknown dtype code in numpy.pxd (%d)";
-static char __pyx_k_58[] = "^";
-static PyObject *__pyx_kp_59;
-static PyObject *__pyx_kp_62;
-static PyObject *__pyx_kp_63;
-static PyObject *__pyx_kp_64;
-static char __pyx_k_59[] = "Format string allocated too short, see comment in numpy.pxd";
-static char __pyx_k_60[] = ">";
-static char __pyx_k_61[] = "<";
-static char __pyx_k_62[] = "Non-native byte order not supported";
-static char __pyx_k_63[] = "Format string allocated too short.";
-static char __pyx_k_64[] = "unknown dtype code in numpy.pxd (%d)";
-
-/* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":85
+static char __pyx_k_1[] = "Seems we've run out of of memory";
+static char __pyx_k_2[] = "We've run out of of memory";
+static char __pyx_k_3[] = "ndarray is not C contiguous";
+static char __pyx_k_4[] = "ndarray is not Fortran contiguous";
+static char __pyx_k_5[] = "Non-native byte order not supported";
+static char __pyx_k_6[] = "unknown dtype code in numpy.pxd (%d)";
+static char __pyx_k_7[] = "Format string allocated too short, see comment in numpy.pxd";
+static char __pyx_k_8[] = "Format string allocated too short.";
+static char __pyx_k_9[] = "\nBinding for libsvm[1]\n---------------------\nWe do not use the binding that ships with libsvm because we need to\naccess svm_model.sv_coeff (and other fields), but libsvm does not\nprovide an accessor. Our solution is to export svm_model and access it\nmanually, this is done un function see svm_train_wrap.\n\nlibsvm uses an sparse representation for the training vectors. In\nmethod dense_to_sparse we translate a dense matrix representation as\nthose produced by NumPy to a sparse representation that libsvm can\nunderstand.\n\nWe define arrays to be the same type as those in libsvm, usually of \ntype C double and C int.\n\nLow-level memory management is done in libsvm_helper.c. If we happen\nto run out of memory a MemoryError will be raised. In practice this is\nnot very helpful since hight changes are malloc fails inside svm.cpp,\nwhere no sort of memory checks are done.\n\nThese are low-level routines, not meant to be used directly. See\nscikits.learn.svm for a higher-level API.\n\n[1] http://www.csie.ntu.edu.tw/~cjlin/libsvm/\n\nNotes\n-----\nMaybe we could speed it a bit further by decorating functions with\n@cython.boundscheck(False), but probably it is not worth since all\nwork is done in lisvm_helper.c\nAlso, the signature mode='c' is somewhat superficial, since we already\ncheck that arrays are C-contiguous in svm.py\n\nAuthors\n-------\n2010: Fabian Pedregosa <fabian.pedregosa@inria.fr>\n Gael Varoquaux <gael.varoquaux@normalesup.org>\n";
+static char __pyx_k_10[] = "train_wrap (line 85)";
+static char __pyx_k_11[] = "predict_from_model_wrap (line 197)";
+static char __pyx_k_12[] = "predict_from_model_wrap";
+static char __pyx_k_13[] = "predict_prob_from_model_wrap (line 260)";
+static char __pyx_k_14[] = "predict_prob_from_model_wrap";
+static char __pyx_k__B[] = "B";
+static char __pyx_k__C[] = "C";
+static char __pyx_k__H[] = "H";
+static char __pyx_k__I[] = "I";
+static char __pyx_k__L[] = "L";
+static char __pyx_k__O[] = "O";
+static char __pyx_k__Q[] = "Q";
+static char __pyx_k__T[] = "T";
+static char __pyx_k__X[] = "X";
+static char __pyx_k__Y[] = "Y";
+static char __pyx_k__b[] = "b";
+static char __pyx_k__d[] = "d";
+static char __pyx_k__f[] = "f";
+static char __pyx_k__g[] = "g";
+static char __pyx_k__h[] = "h";
+static char __pyx_k__i[] = "i";
+static char __pyx_k__l[] = "l";
+static char __pyx_k__p[] = "p";
+static char __pyx_k__q[] = "q";
+static char __pyx_k__SV[] = "SV";
+static char __pyx_k__Zd[] = "Zd";
+static char __pyx_k__Zf[] = "Zf";
+static char __pyx_k__Zg[] = "Zg";
+static char __pyx_k__np[] = "np";
+static char __pyx_k__nu[] = "nu";
+static char __pyx_k__buf[] = "buf";
+static char __pyx_k__eps[] = "eps";
+static char __pyx_k__nSV[] = "nSV";
+static char __pyx_k__obj[] = "obj";
+static char __pyx_k__base[] = "base";
+static char __pyx_k__data[] = "data";
+static char __pyx_k__ndim[] = "ndim";
+static char __pyx_k__coef0[] = "coef0";
+static char __pyx_k__descr[] = "descr";
+static char __pyx_k__dtype[] = "dtype";
+static char __pyx_k__empty[] = "empty";
+static char __pyx_k__gamma[] = "gamma";
+static char __pyx_k__int32[] = "int32";
+static char __pyx_k__label[] = "label";
+static char __pyx_k__names[] = "names";
+static char __pyx_k__numpy[] = "numpy";
+static char __pyx_k__probA[] = "probA";
+static char __pyx_k__probB[] = "probB";
+static char __pyx_k__range[] = "range";
+static char __pyx_k__shape[] = "shape";
+static char __pyx_k__degree[] = "degree";
+static char __pyx_k__fields[] = "fields";
+static char __pyx_k__format[] = "format";
+static char __pyx_k__resize[] = "resize";
+static char __pyx_k__weight[] = "weight";
+static char __pyx_k__float64[] = "float64";
+static char __pyx_k__strides[] = "strides";
+static char __pyx_k__sv_coef[] = "sv_coef";
+static char __pyx_k____main__[] = "__main__";
+static char __pyx_k____test__[] = "__test__";
+static char __pyx_k__itemsize[] = "itemsize";
+static char __pyx_k__readonly[] = "readonly";
+static char __pyx_k__refcheck[] = "refcheck";
+static char __pyx_k__svm_type[] = "svm_type";
+static char __pyx_k__type_num[] = "type_num";
+static char __pyx_k__byteorder[] = "byteorder";
+static char __pyx_k__intercept[] = "intercept";
+static char __pyx_k__nclass_SV[] = "nclass_SV";
+static char __pyx_k__nr_weight[] = "nr_weight";
+static char __pyx_k__shrinking[] = "shrinking";
+static char __pyx_k__ValueError[] = "ValueError";
+static char __pyx_k__cache_size[] = "cache_size";
+static char __pyx_k__suboffsets[] = "suboffsets";
+static char __pyx_k__train_wrap[] = "train_wrap";
+static char __pyx_k__MemoryError[] = "MemoryError";
+static char __pyx_k__kernel_type[] = "kernel_type";
+static char __pyx_k__probability[] = "probability";
+static char __pyx_k__RuntimeError[] = "RuntimeError";
+static char __pyx_k__weight_label[] = "weight_label";
+static PyObject *__pyx_kp_s_1;
+static PyObject *__pyx_kp_u_10;
+static PyObject *__pyx_kp_u_11;
+static PyObject *__pyx_n_s_12;
+static PyObject *__pyx_kp_u_13;
+static PyObject *__pyx_n_s_14;
+static PyObject *__pyx_kp_s_2;
+static PyObject *__pyx_kp_u_3;
+static PyObject *__pyx_kp_u_4;
+static PyObject *__pyx_kp_u_5;
+static PyObject *__pyx_kp_u_6;
+static PyObject *__pyx_kp_u_7;
+static PyObject *__pyx_kp_u_8;
+static PyObject *__pyx_n_s__C;
+static PyObject *__pyx_n_s__MemoryError;
+static PyObject *__pyx_n_s__RuntimeError;
+static PyObject *__pyx_n_s__SV;
+static PyObject *__pyx_n_s__T;
+static PyObject *__pyx_n_s__ValueError;
+static PyObject *__pyx_n_s__X;
+static PyObject *__pyx_n_s__Y;
+static PyObject *__pyx_n_s____main__;
+static PyObject *__pyx_n_s____test__;
+static PyObject *__pyx_n_s__base;
+static PyObject *__pyx_n_s__buf;
+static PyObject *__pyx_n_s__byteorder;
+static PyObject *__pyx_n_s__cache_size;
+static PyObject *__pyx_n_s__coef0;
+static PyObject *__pyx_n_s__data;
+static PyObject *__pyx_n_s__degree;
+static PyObject *__pyx_n_s__descr;
+static PyObject *__pyx_n_s__dtype;
+static PyObject *__pyx_n_s__empty;
+static PyObject *__pyx_n_s__eps;
+static PyObject *__pyx_n_s__fields;
+static PyObject *__pyx_n_s__float64;
+static PyObject *__pyx_n_s__format;
+static PyObject *__pyx_n_s__gamma;
+static PyObject *__pyx_n_s__int32;
+static PyObject *__pyx_n_s__intercept;
+static PyObject *__pyx_n_s__itemsize;
+static PyObject *__pyx_n_s__kernel_type;
+static PyObject *__pyx_n_s__label;
+static PyObject *__pyx_n_s__nSV;
+static PyObject *__pyx_n_s__names;
+static PyObject *__pyx_n_s__nclass_SV;
+static PyObject *__pyx_n_s__ndim;
+static PyObject *__pyx_n_s__np;
+static PyObject *__pyx_n_s__nr_weight;
+static PyObject *__pyx_n_s__nu;
+static PyObject *__pyx_n_s__numpy;
+static PyObject *__pyx_n_s__obj;
+static PyObject *__pyx_n_s__p;
+static PyObject *__pyx_n_s__probA;
+static PyObject *__pyx_n_s__probB;
+static PyObject *__pyx_n_s__probability;
+static PyObject *__pyx_n_s__range;
+static PyObject *__pyx_n_s__readonly;
+static PyObject *__pyx_n_s__refcheck;
+static PyObject *__pyx_n_s__resize;
+static PyObject *__pyx_n_s__shape;
+static PyObject *__pyx_n_s__shrinking;
+static PyObject *__pyx_n_s__strides;
+static PyObject *__pyx_n_s__suboffsets;
+static PyObject *__pyx_n_s__sv_coef;
+static PyObject *__pyx_n_s__svm_type;
+static PyObject *__pyx_n_s__train_wrap;
+static PyObject *__pyx_n_s__type_num;
+static PyObject *__pyx_n_s__weight;
+static PyObject *__pyx_n_s__weight_label;
+static PyObject *__pyx_int_0;
+static PyObject *__pyx_int_15;
+
+/* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":85
* # Wrapper functions
*
* def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X, # <<<<<<<<<<<<<<
@@ -860,7 +818,7 @@ static char __pyx_k_64[] = "unknown dtype code in numpy.pxd (%d)";
*/
static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6libsvm_train_wrap[] = "\n Wrapper for svm_train in libsvm\n\n Parameters\n ----------\n X: array-like, dtype=float, size=[N, D]\n\n Y: array, dtype=float, size=[N]\n target vector\n\n Optional Parameters\n -------------------\n See scikits.learn.svm.predict for a complete list of parameters.\n\n Return\n ------\n sv_coef: array of coeficients for support vector in decision\n function (aka alphas)\n rho : array\n constants in decision functions\n SV : array-like\n support vectors\n TODO\n ";
+static char __pyx_doc_6libsvm_train_wrap[] = "\n Wrap svm_train from libsvm\n\n Parameters\n ----------\n X: array-like, dtype=float, size=[N, D]\n\n Y: array, dtype=float, size=[N]\n target vector\n\n ...\n\n Notes\n -------------------\n See scikits.learn.svm.predict for a complete list of parameters.\n\n Return\n ------\n sv_coef: array of coeficients for support vector in decision\n function (aka alphas)\n intercept : array\n constants in decision functions\n SV : array-like\n support vectors\n TODO\n ";
static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyArrayObject *__pyx_v_X = 0;
PyArrayObject *__pyx_v_Y = 0;
@@ -872,8 +830,12 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
double __pyx_v_eps;
double __pyx_v_C;
int __pyx_v_nr_weight;
+ PyArrayObject *__pyx_v_SV = 0;
+ PyArrayObject *__pyx_v_sv_coef = 0;
+ PyArrayObject *__pyx_v_intercept = 0;
PyArrayObject *__pyx_v_weight_label = 0;
PyArrayObject *__pyx_v_weight = 0;
+ PyArrayObject *__pyx_v_nclass_SV = 0;
double __pyx_v_nu;
double __pyx_v_cache_size;
double __pyx_v_p;
@@ -883,12 +845,8 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
struct svm_problem *__pyx_v_problem;
struct svm_model *__pyx_v_model;
char *__pyx_v_error_msg;
- int __pyx_v_nSV;
+ int __pyx_v_SV_len;
int __pyx_v_nr;
- PyArrayObject *__pyx_v_sv_coef;
- PyArrayObject *__pyx_v_rho;
- PyArrayObject *__pyx_v_SV;
- PyArrayObject *__pyx_v_nclass_SV;
PyArrayObject *__pyx_v_label;
PyArrayObject *__pyx_v_probA;
PyArrayObject *__pyx_v_probB;
@@ -898,28 +856,20 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
Py_buffer __pyx_bstruct_weight_label;
Py_ssize_t __pyx_bstride_0_weight_label = 0;
Py_ssize_t __pyx_bshape_0_weight_label = 0;
- Py_buffer __pyx_bstruct_probA;
- Py_ssize_t __pyx_bstride_0_probA = 0;
- Py_ssize_t __pyx_bshape_0_probA = 0;
- Py_buffer __pyx_bstruct_probB;
- Py_ssize_t __pyx_bstride_0_probB = 0;
- Py_ssize_t __pyx_bshape_0_probB = 0;
- Py_buffer __pyx_bstruct_label;
- Py_ssize_t __pyx_bstride_0_label = 0;
- Py_ssize_t __pyx_bshape_0_label = 0;
- Py_buffer __pyx_bstruct_sv_coef;
- Py_ssize_t __pyx_bstride_0_sv_coef = 0;
- Py_ssize_t __pyx_bstride_1_sv_coef = 0;
- Py_ssize_t __pyx_bshape_0_sv_coef = 0;
- Py_ssize_t __pyx_bshape_1_sv_coef = 0;
- Py_buffer __pyx_bstruct_rho;
- Py_ssize_t __pyx_bstride_0_rho = 0;
- Py_ssize_t __pyx_bshape_0_rho = 0;
Py_buffer __pyx_bstruct_SV;
Py_ssize_t __pyx_bstride_0_SV = 0;
Py_ssize_t __pyx_bstride_1_SV = 0;
Py_ssize_t __pyx_bshape_0_SV = 0;
Py_ssize_t __pyx_bshape_1_SV = 0;
+ Py_buffer __pyx_bstruct_label;
+ Py_ssize_t __pyx_bstride_0_label = 0;
+ Py_ssize_t __pyx_bshape_0_label = 0;
+ Py_buffer __pyx_bstruct_probB;
+ Py_ssize_t __pyx_bstride_0_probB = 0;
+ Py_ssize_t __pyx_bshape_0_probB = 0;
+ Py_buffer __pyx_bstruct_intercept;
+ Py_ssize_t __pyx_bstride_0_intercept = 0;
+ Py_ssize_t __pyx_bshape_0_intercept = 0;
Py_buffer __pyx_bstruct_Y;
Py_ssize_t __pyx_bstride_0_Y = 0;
Py_ssize_t __pyx_bshape_0_Y = 0;
@@ -931,32 +881,41 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
Py_buffer __pyx_bstruct_nclass_SV;
Py_ssize_t __pyx_bstride_0_nclass_SV = 0;
Py_ssize_t __pyx_bshape_0_nclass_SV = 0;
+ Py_buffer __pyx_bstruct_sv_coef;
+ Py_ssize_t __pyx_bstride_0_sv_coef = 0;
+ Py_ssize_t __pyx_bstride_1_sv_coef = 0;
+ Py_ssize_t __pyx_bshape_0_sv_coef = 0;
+ Py_ssize_t __pyx_bshape_1_sv_coef = 0;
+ Py_buffer __pyx_bstruct_probA;
+ Py_ssize_t __pyx_bstride_0_probA = 0;
+ Py_ssize_t __pyx_bshape_0_probA = 0;
PyObject *__pyx_r = NULL;
- PyObject *__pyx_1 = 0;
- PyObject *__pyx_2 = 0;
int __pyx_t_1;
- PyObject *__pyx_t_2 = NULL;
- PyObject *__pyx_t_3 = NULL;
+ int __pyx_t_2;
+ int __pyx_t_3;
PyObject *__pyx_t_4 = NULL;
PyObject *__pyx_t_5 = NULL;
- PyArrayObject *__pyx_t_6 = NULL;
- int __pyx_t_7;
+ PyObject *__pyx_t_6 = NULL;
+ PyObject *__pyx_t_7 = NULL;
PyObject *__pyx_t_8 = NULL;
- PyObject *__pyx_t_9 = NULL;
- PyObject *__pyx_t_10 = NULL;
- PyArrayObject *__pyx_t_11 = NULL;
- PyArrayObject *__pyx_t_12 = NULL;
- PyArrayObject *__pyx_t_13 = NULL;
+ PyArrayObject *__pyx_t_9 = NULL;
+ int __pyx_t_10;
+ PyObject *__pyx_t_11 = NULL;
+ PyObject *__pyx_t_12 = NULL;
+ PyObject *__pyx_t_13 = NULL;
PyArrayObject *__pyx_t_14 = NULL;
PyArrayObject *__pyx_t_15 = NULL;
- PyArrayObject *__pyx_t_16 = NULL;
- static PyObject **__pyx_pyargnames[] = {&__pyx_kp_X,&__pyx_kp_Y,&__pyx_kp_svm_type,&__pyx_kp_kernel_type,&__pyx_kp_degree,&__pyx_kp_gamma,&__pyx_kp_coef0,&__pyx_kp_eps,&__pyx_kp_C,&__pyx_kp_nr_weight,&__pyx_kp_weight_label,&__pyx_kp_weight,&__pyx_kp_nu,&__pyx_kp_cache_size,&__pyx_kp_p,&__pyx_kp_shrinking,&__pyx_kp_probability,0};
- __Pyx_SetupRefcountContext("train_wrap");
+ static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__X,&__pyx_n_s__Y,&__pyx_n_s__svm_type,&__pyx_n_s__kernel_type,&__pyx_n_s__degree,&__pyx_n_s__gamma,&__pyx_n_s__coef0,&__pyx_n_s__eps,&__pyx_n_s__C,&__pyx_n_s__nr_weight,&__pyx_n_s__SV,&__pyx_n_s__sv_coef,&__pyx_n_s__intercept,&__pyx_n_s__weight_label,&__pyx_n_s__weight,&__pyx_n_s__nclass_SV,&__pyx_n_s__nu,&__pyx_n_s__cache_size,&__pyx_n_s__p,&__pyx_n_s__shrinking,&__pyx_n_s__probability,0};
+ __Pyx_RefNannySetupContext("train_wrap");
__pyx_self = __pyx_self;
if (unlikely(__pyx_kwds)) {
Py_ssize_t kw_args = PyDict_Size(__pyx_kwds);
- PyObject* values[17] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
+ PyObject* values[21] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
switch (PyTuple_GET_SIZE(__pyx_args)) {
+ case 21: values[20] = PyTuple_GET_ITEM(__pyx_args, 20);
+ case 20: values[19] = PyTuple_GET_ITEM(__pyx_args, 19);
+ case 19: values[18] = PyTuple_GET_ITEM(__pyx_args, 18);
+ case 18: values[17] = PyTuple_GET_ITEM(__pyx_args, 17);
case 17: values[16] = PyTuple_GET_ITEM(__pyx_args, 16);
case 16: values[15] = PyTuple_GET_ITEM(__pyx_args, 15);
case 15: values[14] = PyTuple_GET_ITEM(__pyx_args, 14);
@@ -979,104 +938,128 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
}
switch (PyTuple_GET_SIZE(__pyx_args)) {
case 0:
- values[0] = PyDict_GetItem(__pyx_kwds, __pyx_kp_X);
+ values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__X);
if (likely(values[0])) kw_args--;
else goto __pyx_L5_argtuple_error;
case 1:
- values[1] = PyDict_GetItem(__pyx_kwds, __pyx_kp_Y);
+ values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__Y);
if (likely(values[1])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 2:
- values[2] = PyDict_GetItem(__pyx_kwds, __pyx_kp_svm_type);
+ values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__svm_type);
if (likely(values[2])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 3:
- values[3] = PyDict_GetItem(__pyx_kwds, __pyx_kp_kernel_type);
+ values[3] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__kernel_type);
if (likely(values[3])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 4:
- values[4] = PyDict_GetItem(__pyx_kwds, __pyx_kp_degree);
+ values[4] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__degree);
if (likely(values[4])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 5:
- values[5] = PyDict_GetItem(__pyx_kwds, __pyx_kp_gamma);
+ values[5] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__gamma);
if (likely(values[5])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 6:
- values[6] = PyDict_GetItem(__pyx_kwds, __pyx_kp_coef0);
+ values[6] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__coef0);
if (likely(values[6])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 7:
- values[7] = PyDict_GetItem(__pyx_kwds, __pyx_kp_eps);
+ values[7] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__eps);
if (likely(values[7])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 8:
- values[8] = PyDict_GetItem(__pyx_kwds, __pyx_kp_C);
+ values[8] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__C);
if (likely(values[8])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 9:
- values[9] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_weight);
+ values[9] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nr_weight);
if (likely(values[9])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 10:
- values[10] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight_label);
+ values[10] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__SV);
if (likely(values[10])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 11:
- values[11] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight);
+ values[11] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__sv_coef);
if (likely(values[11])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 12:
- values[12] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nu);
+ values[12] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__intercept);
if (likely(values[12])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 13:
- values[13] = PyDict_GetItem(__pyx_kwds, __pyx_kp_cache_size);
+ values[13] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight_label);
if (likely(values[13])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 14:
- values[14] = PyDict_GetItem(__pyx_kwds, __pyx_kp_p);
+ values[14] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight);
if (likely(values[14])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 15:
- values[15] = PyDict_GetItem(__pyx_kwds, __pyx_kp_shrinking);
+ values[15] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nclass_SV);
if (likely(values[15])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 16:
- values[16] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probability);
+ values[16] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nu);
if (likely(values[16])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 17:
+ values[17] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__cache_size);
+ if (likely(values[17])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 18:
+ values[18] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__p);
+ if (likely(values[18])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 19:
+ values[19] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__shrinking);
+ if (likely(values[19])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 20:
+ values[20] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probability);
+ if (likely(values[20])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
}
if (unlikely(kw_args > 0)) {
@@ -1092,14 +1075,18 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
__pyx_v_eps = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_C = __pyx_PyFloat_AsDouble(values[8]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_nr_weight = __Pyx_PyInt_AsInt(values[9]); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_weight_label = ((PyArrayObject *)values[10]);
- __pyx_v_weight = ((PyArrayObject *)values[11]);
- __pyx_v_nu = __pyx_PyFloat_AsDouble(values[12]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[13]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_p = __pyx_PyFloat_AsDouble(values[14]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[15]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_probability = __Pyx_PyInt_AsInt(values[16]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- } else if (PyTuple_GET_SIZE(__pyx_args) != 17) {
+ __pyx_v_SV = ((PyArrayObject *)values[10]);
+ __pyx_v_sv_coef = ((PyArrayObject *)values[11]);
+ __pyx_v_intercept = ((PyArrayObject *)values[12]);
+ __pyx_v_weight_label = ((PyArrayObject *)values[13]);
+ __pyx_v_weight = ((PyArrayObject *)values[14]);
+ __pyx_v_nclass_SV = ((PyArrayObject *)values[15]);
+ __pyx_v_nu = __pyx_PyFloat_AsDouble(values[16]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[17]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_p = __pyx_PyFloat_AsDouble(values[18]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[19]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_probability = __Pyx_PyInt_AsInt(values[20]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 96; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ } else if (PyTuple_GET_SIZE(__pyx_args) != 21) {
goto __pyx_L5_argtuple_error;
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@@ -1112,43 +1099,55 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
__pyx_v_eps = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 7)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_C = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 8)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_nr_weight = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 9)); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_weight_label = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 10));
- __pyx_v_weight = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 11));
- __pyx_v_nu = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 12)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_cache_size = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 13)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_p = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 14)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_shrinking = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 15)); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_probability = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 16)); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_SV = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 10));
+ __pyx_v_sv_coef = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 11));
+ __pyx_v_intercept = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 12));
+ __pyx_v_weight_label = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 13));
+ __pyx_v_weight = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 14));
+ __pyx_v_nclass_SV = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 15));
+ __pyx_v_nu = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 16)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 17)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_shrinking = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 19)); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_probability = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 20)); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 96; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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goto __pyx_L4_argument_unpacking_done;
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+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
__Pyx_AddTraceback("libsvm.train_wrap");
return NULL;
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- __pyx_v_rho = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
- __pyx_v_SV = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
- __pyx_v_nclass_SV = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
+ __Pyx_INCREF((PyObject *)__pyx_v_X);
+ __Pyx_INCREF((PyObject *)__pyx_v_Y);
+ __Pyx_INCREF((PyObject *)__pyx_v_SV);
+ __Pyx_INCREF((PyObject *)__pyx_v_sv_coef);
+ __Pyx_INCREF((PyObject *)__pyx_v_intercept);
+ __Pyx_INCREF((PyObject *)__pyx_v_weight_label);
+ __Pyx_INCREF((PyObject *)__pyx_v_weight);
+ __Pyx_INCREF((PyObject *)__pyx_v_nclass_SV);
__pyx_v_label = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
__pyx_v_probA = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
__pyx_v_probB = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
- __pyx_bstruct_sv_coef.buf = NULL;
- __pyx_bstruct_rho.buf = NULL;
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- __pyx_bstruct_nclass_SV.buf = NULL;
__pyx_bstruct_label.buf = NULL;
__pyx_bstruct_probA.buf = NULL;
__pyx_bstruct_probB.buf = NULL;
__pyx_bstruct_X.buf = NULL;
__pyx_bstruct_Y.buf = NULL;
+ __pyx_bstruct_SV.buf = NULL;
+ __pyx_bstruct_sv_coef.buf = NULL;
+ __pyx_bstruct_intercept.buf = NULL;
__pyx_bstruct_weight_label.buf = NULL;
__pyx_bstruct_weight.buf = NULL;
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if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_X), __pyx_ptype_5numpy_ndarray, 1, "X", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_Y), __pyx_ptype_5numpy_ndarray, 1, "Y", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 86; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_weight_label), __pyx_ptype_5numpy_ndarray, 1, "weight_label", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_weight), __pyx_ptype_5numpy_ndarray, 1, "weight", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_SV), __pyx_ptype_5numpy_ndarray, 1, "SV", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_sv_coef), __pyx_ptype_5numpy_ndarray, 1, "sv_coef", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_intercept), __pyx_ptype_5numpy_ndarray, 1, "intercept", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_weight_label), __pyx_ptype_5numpy_ndarray, 1, "weight_label", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_weight), __pyx_ptype_5numpy_ndarray, 1, "weight", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 93; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_nclass_SV), __pyx_ptype_5numpy_ndarray, 1, "nclass_SV", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 94; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_BufFmt_StackElem __pyx_stack[1];
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@@ -1163,18 +1162,42 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
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{
__Pyx_BufFmt_StackElem __pyx_stack[1];
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+ }
+ __pyx_bstride_0_SV = __pyx_bstruct_SV.strides[0]; __pyx_bstride_1_SV = __pyx_bstruct_SV.strides[1];
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+ {
+ __Pyx_BufFmt_StackElem __pyx_stack[1];
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+ }
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+ __pyx_bshape_0_sv_coef = __pyx_bstruct_sv_coef.shape[0]; __pyx_bshape_1_sv_coef = __pyx_bstruct_sv_coef.shape[1];
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{
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}
__pyx_bstride_0_weight = __pyx_bstruct_weight.strides[0];
__pyx_bshape_0_weight = __pyx_bstruct_weight.shape[0];
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":124
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":130
*
* # set libsvm problem
* problem = set_problem(X.data, Y.data, X.shape) # <<<<<<<<<<<<<<
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*/
__pyx_v_problem = set_problem(__pyx_v_X->data, __pyx_v_Y->data, __pyx_v_X->dimensions);
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":130
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":136
* coef0, nu, cache_size,
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@@ -1192,43 +1215,45 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":133
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":139
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* raise MemoryError("Seems we've run out of of memory")
* error_msg = svm_check_parameter(problem, param);
*/
- if (!(__pyx_v_param == NULL)) {
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+ __pyx_t_1 = (__pyx_v_param == NULL);
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}
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":134
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":140
* # check parameters
* if (param == NULL or problem == NULL):
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*/
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goto __pyx_L6;
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__pyx_L6:;
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":135
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":141
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@@ -1237,17 +1262,17 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":136
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":142
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":137
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":143
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":138
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":139
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goto __pyx_L7;
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__pyx_L7:;
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":142
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":144
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":145
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 6:
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 9:
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+ values[9] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__eps);
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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else {
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 11:
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+ values[11] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nr_weight);
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 13:
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+ values[13] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight);
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 14:
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 16:
- values[16] = PyDict_GetItem(__pyx_kwds, __pyx_kp_p);
+ values[16] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__p);
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 17:
- values[17] = PyDict_GetItem(__pyx_kwds, __pyx_kp_shrinking);
+ values[17] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__shrinking);
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 18:
- values[18] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probability);
+ values[18] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probability);
if (likely(values[18])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 19:
- values[19] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_class);
+ values[19] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nSV);
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 20:
- values[20] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nSV);
+ values[20] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__label);
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 21:
- values[21] = PyDict_GetItem(__pyx_kwds, __pyx_kp_label);
+ values[21] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probA);
if (likely(values[21])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 22:
- values[22] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probA);
+ values[22] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probB);
if (likely(values[22])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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- values[23] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probB);
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 23); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_degree = __Pyx_PyInt_AsInt(values[6]); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 201; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_gamma = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 202; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_eps = __pyx_PyFloat_AsDouble(values[9]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 202; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__pyx_v_weight = ((PyArrayObject *)values[13]);
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+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[15]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 206; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":238
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":244
* coef0, nu, cache_size, C, eps, p, shrinking,
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- * model = set_model(param, nr_class, SV.data, SV.shape, sv_coef.strides,
- * sv_coef.data, rho.data, nSV.data, label.data,
+ *
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*/
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+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":248
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+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":250
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":248
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":249
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PyArrayObject *__pyx_v_sv_coef = 0;
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int __pyx_v_kernel_type;
int __pyx_v_degree;
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Py_ssize_t __pyx_bshape_0_sv_coef = 0;
Py_ssize_t __pyx_bshape_1_sv_coef = 0;
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PyObject *__pyx_t_4 = NULL;
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 2:
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 3:
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 4:
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 5:
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- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 6:
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 8:
- values[8] = PyDict_GetItem(__pyx_kwds, __pyx_kp_coef0);
+ values[8] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__coef0);
if (likely(values[8])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 9:
- values[9] = PyDict_GetItem(__pyx_kwds, __pyx_kp_eps);
+ values[9] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__eps);
if (likely(values[9])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 10:
- values[10] = PyDict_GetItem(__pyx_kwds, __pyx_kp_C);
+ values[10] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__C);
if (likely(values[10])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 11:
- values[11] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_weight);
+ values[11] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nr_weight);
if (likely(values[11])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 12:
- values[12] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight_label);
+ values[12] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight_label);
if (likely(values[12])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 13:
- values[13] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight);
+ values[13] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight);
if (likely(values[13])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 14:
- values[14] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nu);
+ values[14] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nu);
if (likely(values[14])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 15:
- values[15] = PyDict_GetItem(__pyx_kwds, __pyx_kp_cache_size);
+ values[15] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__cache_size);
if (likely(values[15])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 16:
- values[16] = PyDict_GetItem(__pyx_kwds, __pyx_kp_p);
+ values[16] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__p);
if (likely(values[16])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 17:
- values[17] = PyDict_GetItem(__pyx_kwds, __pyx_kp_shrinking);
+ values[17] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__shrinking);
if (likely(values[17])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 18:
- values[18] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probability);
+ values[18] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probability);
if (likely(values[18])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 19:
- values[19] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_class);
+ values[19] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nSV);
if (likely(values[19])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 20:
- values[20] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nSV);
+ values[20] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__label);
if (likely(values[20])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 21:
- values[21] = PyDict_GetItem(__pyx_kwds, __pyx_kp_label);
+ values[21] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probA);
if (likely(values[21])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 22:
- values[22] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probA);
+ values[22] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probB);
if (likely(values[22])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- }
- case 23:
- values[23] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probB);
- if (likely(values[23])) kw_args--;
- else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 23); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
}
if (unlikely(kw_args > 0)) {
- if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "predict_prob_from_model_wrap") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "predict_prob_from_model_wrap") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__pyx_v_T = ((PyArrayObject *)values[0]);
__pyx_v_SV = ((PyArrayObject *)values[1]);
__pyx_v_sv_coef = ((PyArrayObject *)values[2]);
- __pyx_v_rho = ((PyArrayObject *)values[3]);
- __pyx_v_svm_type = __Pyx_PyInt_AsInt(values[4]); if (unlikely((__pyx_v_svm_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_kernel_type = __Pyx_PyInt_AsInt(values[5]); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_degree = __Pyx_PyInt_AsInt(values[6]); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_gamma = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_coef0 = __pyx_PyFloat_AsDouble(values[8]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_eps = __pyx_PyFloat_AsDouble(values[9]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_C = __pyx_PyFloat_AsDouble(values[10]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_nr_weight = __Pyx_PyInt_AsInt(values[11]); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_intercept = ((PyArrayObject *)values[3]);
+ __pyx_v_svm_type = __Pyx_PyInt_AsInt(values[4]); if (unlikely((__pyx_v_svm_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 264; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_kernel_type = __Pyx_PyInt_AsInt(values[5]); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 264; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_degree = __Pyx_PyInt_AsInt(values[6]); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 264; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_gamma = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_coef0 = __pyx_PyFloat_AsDouble(values[8]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_eps = __pyx_PyFloat_AsDouble(values[9]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_C = __pyx_PyFloat_AsDouble(values[10]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 266; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_nr_weight = __Pyx_PyInt_AsInt(values[11]); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 266; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_weight_label = ((PyArrayObject *)values[12]);
__pyx_v_weight = ((PyArrayObject *)values[13]);
- __pyx_v_nu = __pyx_PyFloat_AsDouble(values[14]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[15]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_p = __pyx_PyFloat_AsDouble(values[16]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[17]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_probability = __Pyx_PyInt_AsInt(values[18]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 263; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_nr_class = __Pyx_PyInt_AsInt(values[19]); if (unlikely((__pyx_v_nr_class == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 263; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_nSV = ((PyArrayObject *)values[20]);
- __pyx_v_label = ((PyArrayObject *)values[21]);
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+ __pyx_v_nu = __pyx_PyFloat_AsDouble(values[14]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[15]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_p = __pyx_PyFloat_AsDouble(values[16]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[17]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_probability = __Pyx_PyInt_AsInt(values[18]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 270; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_nSV = ((PyArrayObject *)values[19]);
+ __pyx_v_label = ((PyArrayObject *)values[20]);
+ __pyx_v_probA = ((PyArrayObject *)values[21]);
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goto __pyx_L5_argtuple_error;
} else {
__pyx_v_T = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 0));
__pyx_v_SV = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 1));
__pyx_v_sv_coef = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 2));
- __pyx_v_rho = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 3));
- __pyx_v_svm_type = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 4)); if (unlikely((__pyx_v_svm_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_kernel_type = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 5)); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_degree = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 6)); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_gamma = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 7)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_coef0 = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 8)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_eps = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 9)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_C = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 10)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_nr_weight = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 11)); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_intercept = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 3));
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@@ -3380,7 +3368,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(((PyArrayObject *)__pyx_v_self)));
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":101
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":223
* else:
* info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
* info.shape = <Py_ssize_t*>PyArray_DIMS(self) # <<<<<<<<<<<<<<
@@ -3391,7 +3379,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
}
__pyx_L8:;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":102
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":224
* info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
* info.shape = <Py_ssize_t*>PyArray_DIMS(self)
* info.suboffsets = NULL # <<<<<<<<<<<<<<
@@ -3400,7 +3388,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->suboffsets = NULL;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":103
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":225
* info.shape = <Py_ssize_t*>PyArray_DIMS(self)
* info.suboffsets = NULL
* info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<<
@@ -3409,7 +3397,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->itemsize = PyArray_ITEMSIZE(((PyArrayObject *)__pyx_v_self));
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":104
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":226
* info.suboffsets = NULL
* info.itemsize = PyArray_ITEMSIZE(self)
* info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<<
@@ -3418,7 +3406,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->readonly = (!PyArray_ISWRITEABLE(((PyArrayObject *)__pyx_v_self)));
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":107
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":229
*
* cdef int t
* cdef char* f = NULL # <<<<<<<<<<<<<<
@@ -3427,7 +3415,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_f = NULL;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":108
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":230
* cdef int t
* cdef char* f = NULL
* cdef dtype descr = self.descr # <<<<<<<<<<<<<<
@@ -3437,7 +3425,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
__Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_self)->descr));
__pyx_v_descr = ((PyArrayObject *)__pyx_v_self)->descr;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":112
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":234
* cdef int offset
*
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@@ -3446,21 +3434,23 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_hasfields = PyDataType_HASFIELDS(__pyx_v_descr);
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":114
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":236
* cdef bint hasfields = PyDataType_HASFIELDS(descr)
*
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* # do not call releasebuffer
* info.obj = None
*/
- if ((!__pyx_v_hasfields)) {
- __pyx_t_1 = (!__pyx_v_copy_shape);
+ __pyx_t_2 = (!__pyx_v_hasfields);
+ if (__pyx_t_2) {
+ __pyx_t_3 = (!__pyx_v_copy_shape);
+ __pyx_t_1 = __pyx_t_3;
} else {
- __pyx_t_1 = (!__pyx_v_hasfields);
+ __pyx_t_1 = __pyx_t_2;
}
if (__pyx_t_1) {
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":116
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":238
* if not hasfields and not copy_shape:
* # do not call releasebuffer
* info.obj = None # <<<<<<<<<<<<<<
@@ -3476,7 +3466,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
}
/*else*/ {
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":119
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":241
* else:
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@@ -3491,7 +3481,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
}
__pyx_L11:;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":121
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":243
* info.obj = self
*
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@@ -3501,7 +3491,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
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if (__pyx_t_1) {
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":122
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":244
*
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@@ -3510,286 +3500,322 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_t = __pyx_v_descr->type_num;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":123
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":245
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*/
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+ if (__pyx_t_1) {
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- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":124
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":246
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* if ((descr.byteorder == '>' and little_endian) or
* (descr.byteorder == '<' and not little_endian)): # <<<<<<<<<<<<<<
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*/
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- if (__pyx_t_6) {
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- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":125
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* if ((descr.byteorder == '>' and little_endian) or
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goto __pyx_L13;
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__pyx_L13:;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":126
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* (descr.byteorder == '<' and not little_endian)):
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*/
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- break;
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+ __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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@@ -4770,32 +5147,37 @@ PyMODINIT_FUNC PyInit_libsvm(void); /*proto*/
PyMODINIT_FUNC PyInit_libsvm(void)
#endif
{
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- void* __pyx_refchk = NULL;
- __Pyx_Refnanny = __Pyx_ImportRefcountAPI("refnanny");
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+ __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny");
+ if (!__Pyx_RefNanny)
+ Py_FatalError("failed to import 'refnanny' module");
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+ __pyx_refnanny = __Pyx_RefNanny->SetupContext("PyMODINIT_FUNC PyInit_libsvm(void)", __LINE__, __FILE__);
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+ #if PY_MAJOR_VERSION < 3
+ __pyx_empty_bytes = PyString_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #else
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+ #endif
/*--- Library function declarations ---*/
- __pyx_init_filenames();
/*--- Threads initialization code ---*/
#if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS
#ifdef WITH_THREAD /* Python build with threading support? */
PyEval_InitThreads();
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#endif
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- __pyx_m = Py_InitModule4(__Pyx_NAMESTR("libsvm"), __pyx_methods, __pyx_mdoc, 0, PYTHON_API_VERSION);
+ __pyx_m = Py_InitModule4(__Pyx_NAMESTR("libsvm"), __pyx_methods, __Pyx_DOCSTR(__pyx_k_9), 0, PYTHON_API_VERSION);
#else
__pyx_m = PyModule_Create(&__pyx_moduledef);
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+ /*--- Initialize various global constants etc. ---*/
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+ __pyx_ptype_5numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject)); if (unlikely(!__pyx_ptype_5numpy_broadcast)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 162; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/*--- Function import code ---*/
/*--- Execution code ---*/
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":41
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- __Pyx_DECREF(__pyx_1); __pyx_1 = 0;
+ __pyx_t_1 = __Pyx_Import(((PyObject *)__pyx_n_s__numpy), 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 41; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_1);
+ if (PyObject_SetAttr(__pyx_m, __pyx_n_s__np, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 41; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":1
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+ __Pyx_GOTREF(((PyObject *)__pyx_t_1));
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+ __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+ __pyx_t_3 = PyObject_GetAttr(__pyx_m, __pyx_n_s_12); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_3);
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+ __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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+ if (PyObject_SetAttr(__pyx_m, __pyx_n_s____test__, ((PyObject *)__pyx_t_1)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- /* "/usr/lib/pymodules/python2.6/Cython/Includes/stdlib.pxd":2
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/stdlib.pxd":2
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goto __pyx_L0;
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- __Pyx_XDECREF(__pyx_1);
- __Pyx_AddTraceback("libsvm");
- Py_DECREF(__pyx_m); __pyx_m = 0;
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+ __Pyx_XDECREF(__pyx_t_2);
+ __Pyx_XDECREF(__pyx_t_3);
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+ __Pyx_AddTraceback("init libsvm");
+ Py_DECREF(__pyx_m); __pyx_m = 0;
+ } else if (!PyErr_Occurred()) {
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return;
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+}
+
+static INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
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PyErr_Format(PyExc_SystemError, "Missing type object");
return 0;
}
- if (obj == Py_None || PyObject_TypeCheck(obj, type))
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return 1;
- PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s",
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+ PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
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return 0;
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tmp_tb = tstate->curexc_traceback;
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+}
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+ PyErr_SetString(PyExc_ValueError, "too many values to unpack");
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+ __Pyx_RaiseTooManyValuesError();
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+}
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+static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) {
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+ __Pyx_RaiseNoneNotIterableError();
+ } else if (PyTuple_GET_SIZE(t) < index) {
+ __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t));
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+ __Pyx_RaiseTooManyValuesError();
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+}
static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
const char *name, int exact)
@@ -5578,6 +6041,7 @@ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
return result;
}
+#if PY_MAJOR_VERSION < 3
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
Py_XINCREF(type);
Py_XINCREF(value);
@@ -5633,6 +6097,7 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
}
#endif
}
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__Pyx_ErrRestore(type, value, tb);
return;
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+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
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+ } else if (tb && !PyTraceBack_Check(tb)) {
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+ goto bad;
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+ PyErr_SetObject(type, value);
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+
+bad:
+ return;
+}
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+ } else if (sizeof(npy_intp) == sizeof(long)) {
+ if (is_unsigned)
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+ else
+ return PyInt_FromLong((long)val);
+ } else { /* (sizeof(npy_intp) > sizeof(long)) */
+ if (is_unsigned)
+ return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG)val);
+ else
+ return PyLong_FromLongLong((PY_LONG_LONG)val);
+ }
}
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+ return ::std::complex< float >(x, y);
+ }
+ #else
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+ return x + y*(__pyx_t_float_complex)_Complex_I;
+ }
+ #endif
+#else
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+ __pyx_t_float_complex z;
+ z.real = x;
+ z.imag = y;
+ return z;
+ }
+#endif
+
+#if CYTHON_CCOMPLEX
+#else
+ static INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ return (a.real == b.real) && (a.imag == b.imag);
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ z.real = a.real + b.real;
+ z.imag = a.imag + b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ z.real = a.real - b.real;
+ z.imag = a.imag - b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ z.real = a.real * b.real - a.imag * b.imag;
+ z.imag = a.real * b.imag + a.imag * b.real;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ float denom = b.real * b.real + b.imag * b.imag;
+ z.real = (a.real * b.real + a.imag * b.imag) / denom;
+ z.imag = (a.imag * b.real - a.real * b.imag) / denom;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) {
+ __pyx_t_float_complex z;
+ z.real = -a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+ static INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) {
+ return (a.real == 0) && (a.imag == 0);
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) {
+ __pyx_t_float_complex z;
+ z.real = a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+/*
+ static INLINE float __Pyx_c_absf(__pyx_t_float_complex z) {
+#if HAVE_HYPOT
+ return hypotf(z.real, z.imag);
+#else
+ return sqrtf(z.real*z.real + z.imag*z.imag);
+#endif
+ }
+*/
+#endif
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+ return ::std::complex< double >(x, y);
+ }
+ #else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+ return x + y*(__pyx_t_double_complex)_Complex_I;
+ }
+ #endif
+#else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+ __pyx_t_double_complex z;
+ z.real = x;
+ z.imag = y;
+ return z;
+ }
+#endif
+
+#if CYTHON_CCOMPLEX
+#else
+ static INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ return (a.real == b.real) && (a.imag == b.imag);
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ z.real = a.real + b.real;
+ z.imag = a.imag + b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ z.real = a.real - b.real;
+ z.imag = a.imag - b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ z.real = a.real * b.real - a.imag * b.imag;
+ z.imag = a.real * b.imag + a.imag * b.real;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ double denom = b.real * b.real + b.imag * b.imag;
+ z.real = (a.real * b.real + a.imag * b.imag) / denom;
+ z.imag = (a.imag * b.real - a.real * b.imag) / denom;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) {
+ __pyx_t_double_complex z;
+ z.real = -a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+ static INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) {
+ return (a.real == 0) && (a.imag == 0);
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) {
+ __pyx_t_double_complex z;
+ z.real = a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+/*
+ static INLINE double __Pyx_c_abs(__pyx_t_double_complex z) {
+#if HAVE_HYPOT
+ return hypot(z.real, z.imag);
+#else
+ return sqrt(z.real*z.real + z.imag*z.imag);
+#endif
+ }
+*/
+#endif
+
static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) {
+ const unsigned char neg_one = (unsigned char)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(unsigned char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned char)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (unsigned char)-1;
- if (unlikely(val < 0)) {
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned char");
- return (unsigned char)-1;
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to unsigned char" :
+ "value too large to convert to unsigned char");
}
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to unsigned char");
return (unsigned char)-1;
}
return (unsigned char)val;
@@ -5668,18 +6339,17 @@ static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) {
}
static INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) {
+ const unsigned short neg_one = (unsigned short)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(unsigned short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned short)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (unsigned short)-1;
- if (unlikely(val < 0)) {
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned short");
- return (unsigned short)-1;
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to unsigned short" :
+ "value too large to convert to unsigned short");
}
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to unsigned short");
return (unsigned short)-1;
}
return (unsigned short)val;
@@ -5688,18 +6358,17 @@ static INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) {
}
static INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) {
+ const unsigned int neg_one = (unsigned int)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(unsigned int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned int)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (unsigned int)-1;
- if (unlikely(val < 0)) {
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned int");
- return (unsigned int)-1;
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to unsigned int" :
+ "value too large to convert to unsigned int");
}
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to unsigned int");
return (unsigned int)-1;
}
return (unsigned int)val;
@@ -5708,13 +6377,17 @@ static INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) {
}
static INLINE char __Pyx_PyInt_AsChar(PyObject* x) {
+ const char neg_one = (char)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(char)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (char)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to char");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to char" :
+ "value too large to convert to char");
+ }
return (char)-1;
}
return (char)val;
@@ -5723,13 +6396,17 @@ static INLINE char __Pyx_PyInt_AsChar(PyObject* x) {
}
static INLINE short __Pyx_PyInt_AsShort(PyObject* x) {
+ const short neg_one = (short)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(short)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (short)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to short");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to short" :
+ "value too large to convert to short");
+ }
return (short)-1;
}
return (short)val;
@@ -5738,13 +6415,17 @@ static INLINE short __Pyx_PyInt_AsShort(PyObject* x) {
}
static INLINE int __Pyx_PyInt_AsInt(PyObject* x) {
+ const int neg_one = (int)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(int)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (int)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to int");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to int" :
+ "value too large to convert to int");
+ }
return (int)-1;
}
return (int)val;
@@ -5753,13 +6434,17 @@ static INLINE int __Pyx_PyInt_AsInt(PyObject* x) {
}
static INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) {
+ const signed char neg_one = (signed char)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(signed char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed char)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (signed char)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to signed char");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to signed char" :
+ "value too large to convert to signed char");
+ }
return (signed char)-1;
}
return (signed char)val;
@@ -5768,13 +6453,17 @@ static INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) {
}
static INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) {
+ const signed short neg_one = (signed short)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(signed short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed short)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (signed short)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to signed short");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to signed short" :
+ "value too large to convert to signed short");
+ }
return (signed short)-1;
}
return (signed short)val;
@@ -5783,13 +6472,17 @@ static INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) {
}
static INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) {
+ const signed int neg_one = (signed int)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(signed int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed int)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (signed int)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to signed int");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to signed int" :
+ "value too large to convert to signed int");
+ }
return (signed int)-1;
}
return (signed int)val;
@@ -5798,10 +6491,12 @@ static INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) {
}
static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
+ const unsigned long neg_one = (unsigned long)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
- if (unlikely(val < 0)) {
+ if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned long");
return (unsigned long)-1;
@@ -5809,13 +6504,17 @@ static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
return (unsigned long)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- if (unlikely(Py_SIZE(x) < 0)) {
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned long");
- return (unsigned long)-1;
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to unsigned long");
+ return (unsigned long)-1;
+ }
+ return PyLong_AsUnsignedLong(x);
+ } else {
+ return PyLong_AsLong(x);
}
- return PyLong_AsUnsignedLong(x);
} else {
unsigned long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5827,10 +6526,12 @@ static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
}
static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) {
+ const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
- if (unlikely(val < 0)) {
+ if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned PY_LONG_LONG");
return (unsigned PY_LONG_LONG)-1;
@@ -5838,13 +6539,17 @@ static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x)
return (unsigned PY_LONG_LONG)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- if (unlikely(Py_SIZE(x) < 0)) {
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned PY_LONG_LONG");
- return (unsigned PY_LONG_LONG)-1;
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to unsigned PY_LONG_LONG");
+ return (unsigned PY_LONG_LONG)-1;
+ }
+ return PyLong_AsUnsignedLongLong(x);
+ } else {
+ return PyLong_AsLongLong(x);
}
- return PyLong_AsUnsignedLongLong(x);
} else {
unsigned PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5856,14 +6561,30 @@ static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x)
}
static INLINE long __Pyx_PyInt_AsLong(PyObject* x) {
+ const long neg_one = (long)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to long");
+ return (long)-1;
+ }
return (long)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to long");
+ return (long)-1;
+ }
+ return PyLong_AsUnsignedLong(x);
+ } else {
+ return PyLong_AsLong(x);
+ }
} else {
long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5875,14 +6596,30 @@ static INLINE long __Pyx_PyInt_AsLong(PyObject* x) {
}
static INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) {
+ const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to PY_LONG_LONG");
+ return (PY_LONG_LONG)-1;
+ }
return (PY_LONG_LONG)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLongLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to PY_LONG_LONG");
+ return (PY_LONG_LONG)-1;
+ }
+ return PyLong_AsUnsignedLongLong(x);
+ } else {
+ return PyLong_AsLongLong(x);
+ }
} else {
PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5894,14 +6631,30 @@ static INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) {
}
static INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) {
+ const signed long neg_one = (signed long)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed long");
+ return (signed long)-1;
+ }
return (signed long)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed long");
+ return (signed long)-1;
+ }
+ return PyLong_AsUnsignedLong(x);
+ } else {
+ return PyLong_AsLong(x);
+ }
} else {
signed long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5913,14 +6666,30 @@ static INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) {
}
static INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) {
+ const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed PY_LONG_LONG");
+ return (signed PY_LONG_LONG)-1;
+ }
return (signed PY_LONG_LONG)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLongLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed PY_LONG_LONG");
+ return (signed PY_LONG_LONG)-1;
+ }
+ return PyLong_AsUnsignedLongLong(x);
+ } else {
+ return PyLong_AsLongLong(x);
+ }
} else {
signed PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -6007,7 +6776,6 @@ static void __Pyx_AddTraceback(const char *funcname) {
PyObject *py_srcfile = 0;
PyObject *py_funcname = 0;
PyObject *py_globals = 0;
- PyObject *empty_string = 0;
PyCodeObject *py_code = 0;
PyFrameObject *py_frame = 0;
@@ -6034,12 +6802,6 @@ static void __Pyx_AddTraceback(const char *funcname) {
if (!py_funcname) goto bad;
py_globals = PyModule_GetDict(__pyx_m);
if (!py_globals) goto bad;
- #if PY_MAJOR_VERSION < 3
- empty_string = PyString_FromStringAndSize("", 0);
- #else
- empty_string = PyBytes_FromStringAndSize("", 0);
- #endif
- if (!empty_string) goto bad;
py_code = PyCode_New(
0, /*int argcount,*/
#if PY_MAJOR_VERSION >= 3
@@ -6048,7 +6810,7 @@ static void __Pyx_AddTraceback(const char *funcname) {
0, /*int nlocals,*/
0, /*int stacksize,*/
0, /*int flags,*/
- empty_string, /*PyObject *code,*/
+ __pyx_empty_bytes, /*PyObject *code,*/
__pyx_empty_tuple, /*PyObject *consts,*/
__pyx_empty_tuple, /*PyObject *names,*/
__pyx_empty_tuple, /*PyObject *varnames,*/
@@ -6057,7 +6819,7 @@ static void __Pyx_AddTraceback(const char *funcname) {
py_srcfile, /*PyObject *filename,*/
py_funcname, /*PyObject *name,*/
__pyx_lineno, /*int firstlineno,*/
- empty_string /*PyObject *lnotab*/
+ __pyx_empty_bytes /*PyObject *lnotab*/
);
if (!py_code) goto bad;
py_frame = PyFrame_New(
@@ -6072,7 +6834,6 @@ static void __Pyx_AddTraceback(const char *funcname) {
bad:
Py_XDECREF(py_srcfile);
Py_XDECREF(py_funcname);
- Py_XDECREF(empty_string);
Py_XDECREF(py_code);
Py_XDECREF(py_frame);
}
@@ -6080,7 +6841,7 @@ bad:
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
while (t->p) {
#if PY_MAJOR_VERSION < 3
- if (t->is_unicode && (!t->is_identifier)) {
+ if (t->is_unicode) {
*t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
} else if (t->intern) {
*t->p = PyString_InternFromString(t->s);
@@ -6088,10 +6849,14 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
*t->p = PyString_FromStringAndSize(t->s, t->n - 1);
}
#else /* Python 3+ has unicode identifiers */
- if (t->is_identifier || (t->is_unicode && t->intern)) {
- *t->p = PyUnicode_InternFromString(t->s);
- } else if (t->is_unicode) {
- *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+ if (t->is_unicode | t->is_str) {
+ if (t->intern) {
+ *t->p = PyUnicode_InternFromString(t->s);
+ } else if (t->encoding) {
+ *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+ } else {
+ *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+ }
} else {
*t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
}
@@ -6193,3 +6958,4 @@ static INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) {
}
+#endif /* Py_PYTHON_H */
diff --git a/scikits/learn/src/libsvm.pyx b/scikits/learn/src/libsvm.pyx
index ed081dd989d05f769c22aba5c0beb22e832f875e..0c16dc27e0c2d75abee09e2b372437604702c5c0 100644
--- a/scikits/learn/src/libsvm.pyx
+++ b/scikits/learn/src/libsvm.pyx
@@ -62,11 +62,11 @@ cdef extern from "libsvm_helper.c":
svm_problem *set_problem(char *, char *, np.npy_intp *)
svm_model *set_model(svm_parameter *, int, char *, np.npy_intp *, np.npy_intp *,
char *, char *, char *, char *, char *, char *)
- void copy_sv_coef (char *, svm_model *, np.npy_intp *)
- void copy_rho (char *, svm_model *, np.npy_intp *)
- void copy_SV (char *, svm_model *, np.npy_intp *)
+ void copy_sv_coef (char *, svm_model *, np.npy_intp *)
+ void copy_intercept (char *, svm_model *, np.npy_intp *)
+ void copy_SV (char *, svm_model *, np.npy_intp *)
int copy_predict (char *, svm_model *, np.npy_intp *, char *)
- int copy_prob_predict (char *, svm_model *, np.npy_intp *, char *)
+ int copy_predict_proba (char *, svm_model *, np.npy_intp *, char *)
void copy_nSV (char *, svm_model *)
void copy_label (char *, svm_model *)
void copy_probA(char *, svm_model *, np.npy_intp *)
@@ -86,12 +86,16 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
np.ndarray[np.float64_t, ndim=1, mode='c'] Y, int
svm_type, int kernel_type, int degree, double gamma,
double coef0, double eps, double C, int nr_weight,
- np.ndarray[np.int32_t, ndim=1] weight_label,
- np.ndarray[np.float64_t, ndim=1] weight, double nu,
- double cache_size, double p, int shrinking, int
- probability):
+ np.ndarray[np.float64_t, ndim=2, mode='c'] SV,
+ np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef,
+ np.ndarray[np.float64_t, ndim=1, mode='c'] intercept,
+ np.ndarray[np.int32_t, ndim=1, mode='c'] weight_label,
+ np.ndarray[np.float64_t, ndim=1, mode='c'] weight,
+ np.ndarray[np.int32_t, ndim=1, mode='c'] nclass_SV,
+ double nu, double cache_size, double p, int
+ shrinking, int probability):
"""
- Wrapper for svm_train in libsvm
+ Wrap svm_train from libsvm
Parameters
----------
@@ -100,7 +104,9 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
Y: array, dtype=float, size=[N]
target vector
- Optional Parameters
+ ...
+
+ Notes
-------------------
See scikits.learn.svm.predict for a complete list of parameters.
@@ -108,7 +114,7 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
------
sv_coef: array of coeficients for support vector in decision
function (aka alphas)
- rho : array
+ intercept : array
constants in decision functions
SV : array-like
support vectors
@@ -141,29 +147,29 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
# call svm_train, this does the real work
model = svm_train(problem, param)
- cdef int nSV = get_l(model)
+ cdef int SV_len = get_l(model)
cdef int nr = get_nr(model)
- # copy model.sv_coef
- cdef np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef
- sv_coef = np.empty((nr-1, nSV))
+ # copy model.sv_coef
+ # we create a new array instead of resizing, otherwise
+ # it would not erase previous information
+ sv_coef.resize((nr-1, SV_len), refcheck=False)
copy_sv_coef(sv_coef.data, model, sv_coef.strides)
- # copy model.rho
- cdef np.ndarray[np.float64_t, ndim=1, mode='c'] rho
- rho = np.empty(nr*(nr-1)/2)
- copy_rho(rho.data, model, rho.shape)
+ # copy model.rho into the intercept
+ # the intercept is just model.rho but with sign changed
+ intercept.resize(nr*(nr-1)/2, refcheck=False)
+ copy_intercept(intercept.data, model, intercept.shape)
# copy model.SV
- cdef np.ndarray[np.float64_t, ndim=2, mode='c'] SV
- SV = np.zeros((nSV, X.shape[1]))
- copy_SV(SV.data, model, SV.strides)
+ # we erase any previous information in SV
+ SV.resize((0,0), refcheck=False)
+ SV.resize((SV_len, X.shape[1]), refcheck=False)
+ copy_SV(SV.data, model, SV.shape)
# copy model.nSV
- # name is a bit confusing since we used nSV to denote the total number
- # of support vectors
- cdef np.ndarray[np.int32_t, ndim=1, mode='c'] nclass_SV
- nclass_SV = np.empty((nr), dtype=np.int32)
+ # TODO: do only in classification
+ nclass_SV.resize(nr, refcheck=False)
copy_nSV(nclass_SV.data, model)
# copy label
@@ -185,20 +191,20 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
free_problem(problem)
free_param(param)
- return sv_coef, rho, SV, nr, nclass_SV, label, probA, probB
+ return label, probA, probB
def predict_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
np.ndarray[np.float64_t, ndim=2, mode='c'] SV,
np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef,
np.ndarray[np.float64_t, ndim=1, mode='c']
- rho, int svm_type, int kernel_type, int
+ intercept, int svm_type, int kernel_type, int
degree, double gamma, double coef0, double
eps, double C, int nr_weight,
np.ndarray[np.int32_t, ndim=1] weight_label,
np.ndarray[np.float64_t, ndim=1] weight,
double nu, double cache_size, double p, int
- shrinking, int probability, int nr_class,
+ shrinking, int probability,
np.ndarray[np.int32_t, ndim=1, mode='c'] nSV,
np.ndarray[np.int32_t, ndim=1, mode='c'] label,
np.ndarray[np.float64_t, ndim=1, mode='c'] probA,
@@ -236,9 +242,10 @@ def predict_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
coef0, nu, cache_size, C, eps, p, shrinking,
probability, nr_weight, weight_label.data,
weight.data)
- model = set_model(param, nr_class, SV.data, SV.shape, sv_coef.strides,
- sv_coef.data, rho.data, nSV.data, label.data,
- probA.data, probB.data)
+
+ model = set_model(param, nSV.shape[0], SV.data, SV.shape,
+ sv_coef.strides, sv_coef.data, intercept.data,
+ nSV.data, label.data, probA.data, probB.data)
#TODO: use check_model
dec_values = np.empty(T.shape[0])
if copy_predict(T.data, model, T.shape, dec_values.data) < 0:
@@ -254,13 +261,13 @@ def predict_prob_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
np.ndarray[np.float64_t, ndim=2, mode='c'] SV,
np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef,
np.ndarray[np.float64_t, ndim=1, mode='c']
- rho, int svm_type, int kernel_type, int
+ intercept, int svm_type, int kernel_type, int
degree, double gamma, double coef0, double
eps, double C, int nr_weight,
np.ndarray[np.int32_t, ndim=1] weight_label,
np.ndarray[np.float_t, ndim=1] weight,
double nu, double cache_size, double p, int
- shrinking, int probability, int nr_class,
+ shrinking, int probability,
np.ndarray[np.int32_t, ndim=1, mode='c'] nSV,
np.ndarray[np.int32_t, ndim=1, mode='c'] label,
np.ndarray[np.float64_t, ndim=1, mode='c'] probA,
@@ -298,13 +305,13 @@ def predict_prob_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
coef0, nu, cache_size, C, eps, p, shrinking,
probability, nr_weight, weight_label.data,
weight.data)
- model = set_model(param, nr_class, SV.data, SV.shape, sv_coef.strides,
- sv_coef.data, rho.data, nSV.data, label.data,
+ model = set_model(param, nSV.shape[0], SV.data, SV.shape, sv_coef.strides,
+ sv_coef.data, intercept.data, nSV.data, label.data,
probA.data, probB.data)
cdef int nr = get_nr(model)
dec_values = np.empty((T.shape[0], nr), dtype=np.float64)
- if copy_prob_predict(T.data, model, T.shape, dec_values.data) < 0:
+ if copy_predict_proba(T.data, model, T.shape, dec_values.data) < 0:
raise MemoryError("We've run out of of memory")
# free model and param
free_model_SV(model)
diff --git a/scikits/learn/src/libsvm_helper.c b/scikits/learn/src/libsvm_helper.c
index 56bb31ca6e9da2f1f2c31c2ddd6fbffa575bceb8..136d997f07cc42657130c08c199a4fcbaacf3634 100644
--- a/scikits/learn/src/libsvm_helper.c
+++ b/scikits/learn/src/libsvm_helper.c
@@ -1,4 +1,5 @@
#include <stdlib.h>
+#include <stdio.h>
#include "svm.h"
#include <numpy/arrayobject.h>
@@ -189,7 +190,9 @@ struct svm_model *set_model(struct svm_parameter *param, int nr_class,
t += sv_coef_strides[0];
}
- memcpy(model->rho, rho, m * sizeof(double));
+ for (i=0; i<m; ++i)
+ (model->rho)[i] = -((double *) rho)[i];
+
/*
* just to avoid segfaults, these features are not wrapped but
* svm_destroy_model will try to free them.
@@ -244,9 +247,17 @@ void copy_sv_coef(char *data, struct svm_model *model, npy_intp *strides)
}
}
-void copy_rho(char *data, struct svm_model *model, npy_intp *dims)
+void copy_intercept(char *data, struct svm_model *model, npy_intp *dims)
{
- memcpy(data, model->rho, dims[0] * sizeof(double));
+ /* intercept = -rho */
+ int i, n = dims[0];
+ double t, *ddata = (double *) data;
+ for (i=0; i<n; ++i) {
+ t = model->rho[i];
+ /* we do this to avoid ugly -0.0 */
+ *ddata = (t != 0) ? -t : 0;
+ ++ddata;
+ }
}
/*
@@ -254,19 +265,18 @@ void copy_rho(char *data, struct svm_model *model, npy_intp *dims)
* structures, so we have to do the conversion on the fly and also
* iterate fast over data.
*/
-void copy_SV(char *data, struct svm_model *model, npy_intp *strides)
+void copy_SV(char *data, struct svm_model *model, npy_intp *dims)
{
- register int i, j;
- char *t = data;
- int k, n = model->l;
- npy_intp step = strides[1];
+ int i, j, k, n = model->l;
+ double *t = (double *) data;
+ int step = dims[1] * sizeof(double);
for (i=0; i<n; ++i) {
- k = model->SV[i][0].index;
- for(j=0; k >= 0; ++j) {
- * ((double *) (t + (k-1)*step)) = model->SV[i][j].value;
- k = model->SV[i][j+1].index;
+ k = model->SV[i][0].index - 1;
+ for(j=0; k >=0 ; ++j) {
+ t[k] = model->SV[i][j].value;
+ k = model->SV[i][j+1].index - 1;
}
- t += strides[0];
+ t += dims[1];
}
}
@@ -326,10 +336,9 @@ int copy_predict(char *predict, struct svm_model *model, npy_intp *predict_dims,
return 0;
}
-int copy_prob_predict(char *predict, struct svm_model *model, npy_intp *predict_dims,
+int copy_predict_proba(char *predict, struct svm_model *model, npy_intp *predict_dims,
char *dec_values)
{
- double *t = (double *) dec_values;
register int i;
int n, m;
n = predict_dims[0];
diff --git a/scikits/learn/svm.py b/scikits/learn/svm.py
index acb19a9c757303cf53776ce9cd50d0034a1fdd97..8282943708f73e9b278e43a6c85f00c5d6dd5cd5 100644
--- a/scikits/learn/svm.py
+++ b/scikits/learn/svm.py
@@ -1,13 +1,11 @@
import numpy as np
from . import libsvm, liblinear
-_kernel_types = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
-_svm_types = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
-
class BaseLibsvm(object):
"""
- Base class for classifiers that use support vector machine.
+ Base class for classifiers that use libsvm as library for
+ support vector machine classification and regression.
Should not be used directly, use derived classes instead
@@ -19,17 +17,23 @@ class BaseLibsvm(object):
target vector relative to X
It will be converted to a floating-point array.
"""
- support_ = None
- dual_coef_ = None
- rho_ = None
+ support_ = np.empty((0,0), dtype=np.float64, order='C')
+ dual_coef_ = np.empty((0,0), dtype=np.float64, order='C')
+ intercept_ = np.empty(0, dtype=np.float64, order='C')
+
+ # only used in classification
+ nSV_ = np.empty(0, dtype=np.int32, order='C')
+
+ weight = np.empty(0, dtype=np.float64, order='C')
+ weight_label = np.empty(0, dtype=np.int32, order='C')
- weight = np.empty(0, dtype=np.float64)
- weight_label = np.empty(0, dtype=np.int32)
+ _kernel_types = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
+ _svm_types = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
def __init__(self, impl, kernel, degree, gamma, coef0, cache_size,
eps, C, nr_weight, nu, p, shrinking, probability):
- self.svm = _svm_types.index(impl)
- self.kernel = _kernel_types.index(kernel)
+ self.solver_type = self._svm_types.index(impl)
+ self.kernel = self._kernel_types.index(kernel)
self.degree = degree
self.gamma = gamma
self.coef0 = coef0
@@ -42,9 +46,9 @@ class BaseLibsvm(object):
self.shrinking = int(shrinking)
self.probability = int(probability)
- def fit(self, X, y):
+ def fit(self, X, Y):
"""
- Fit the model with vectors X, Y.
+ Fit the SVM model according to the given training data and parameters.
Parameters
----------
@@ -52,54 +56,86 @@ class BaseLibsvm(object):
Training vector, where nsamples in the number of samples and
nfeatures is the number of features.
Y : array, shape = [nsamples]
- Target vector relative to X
-
+ Target values (integers in classification, real numbers in
+ regression)
"""
X = np.asanyarray(X, dtype=np.float64, order='C')
- y = np.asanyarray(y, dtype=np.float64, order='C')
+ Y = np.asanyarray(Y, dtype=np.float64, order='C')
# check dimensions
- if X.shape[0] != y.shape[0]: raise ValueError("Incompatible shapes")
+ if X.shape[0] != Y.shape[0]: raise ValueError("Incompatible shapes")
if (self.gamma == 0): self.gamma = 1.0/X.shape[0]
- self.dual_coef_, self.rho_, self.support_, self.nclass_, self.nSV_, \
- self.label_, self.probA_, self.probB_ = libsvm.train_wrap(X, y,
- self.svm, self.kernel, self.degree, self.gamma,
- self.coef0, self.eps, self.C, self.nr_weight,
- self.weight_label, self.weight, self.nu, self.cache_size, self.p,
- self.shrinking, int(self.probability))
+ self.label_, self.probA_, self.probB_ = libsvm.train_wrap(X, Y,
+ self.solver_type, self.kernel, self.degree,
+ self.gamma, self.coef0, self.eps, self.C,
+ self.nr_weight, self.support_, self.dual_coef_,
+ self.intercept_, self.weight_label, self.weight,
+ self.nSV_, self.nu, self.cache_size, self.p,
+ self.shrinking,
+ int(self.probability))
return self
+
def predict(self, T):
+ """
+ This function does classification or regression on an array of
+ test vectors T.
+
+ For a classification model, the predicted class for each
+ sample in T is returned.
+ For a regression model, the function value of T calculated is
+ returned.
+
+ For an one-class model, +1 or -1 is returned.
+
+ Parameters
+ ----------
+ T : array-like, shape = [nsamples, nfeatures]
+
+ Returns
+ -------
+ C : array, shape = [nsample]
+ """
T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
return libsvm.predict_from_model_wrap(T, self.support_,
- self.dual_coef_, self.rho_, self.svm,
- self.kernel, self.degree, self.gamma,
- self.coef0, self.eps, self.C, self.nr_weight,
- self.weight_label, self.weight, self.nu,
- self.cache_size,
- self.p, self.shrinking, self.probability,
- self.nclass_, self.nSV_, self.label_,
- self.probA_, self.probB_)
+ self.dual_coef_, self.intercept_,
+ self.solver_type, self.kernel, self.degree,
+ self.gamma, self.coef0, self.eps, self.C,
+ self.nr_weight, self.weight_label, self.weight,
+ self.nu, self.cache_size, self.p,
+ self.shrinking, self.probability,
+ self.nSV_, self.label_, self.probA_,
+ self.probB_)
def predict_proba(self, T):
+ """
+ This function does classification or regression on a test vector T
+ given a model with probability information.
+
+ For a classification model with probability information, this
+ function returns nr_class probability estimates in the array
+ prob_estimates. For regression/one-class SVM, the array prob_estimates
+ is unchanged and the returned value is the same as that of
+ svm_predict.
+
+ Parameters
+ ----------
+ T : array-like, shape = [nsamples, nfeatures]
+ """
if not self.probability:
raise ValueError("probability estimates must be enabled to use this method")
T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
return libsvm.predict_prob_from_model_wrap(T, self.support_,
- self.dual_coef_, self.rho_, self.svm,
+ self.dual_coef_, self.intercept_, self.solver_type,
self.kernel, self.degree, self.gamma,
self.coef0, self.eps, self.C, self.nr_weight,
self.weight_label, self.weight,
self.nu, self.cache_size,
self.p, self.shrinking, self.probability,
- self.nclass_, self.nSV_, self.label_,
+ self.nSV_, self.label_,
self.probA_, self.probB_)
- @property
- def intercept_(self):
- return - self.rho_
-
###
# Public API
# No processing should go into these classes
@@ -115,6 +151,7 @@ class SVC(BaseLibsvm):
X : array-like, shape = [nsamples, nfeatures]
Training vector, where nsamples in the number of samples and
nfeatures is the number of features.
+
Y : array, shape = [nsamples]
Target vector relative to X
@@ -178,27 +215,18 @@ class SVC(BaseLibsvm):
SVR
"""
- def __init__(self, impl='c_svc', kernel='rbf', degree=3,
- gamma=0.0, coef0=0.0, cache_size=100.0, eps=1e-3,
- C=1.0, nr_weight=0, nu=0.5, p=0.1, shrinking=True,
- probability=False):
+ def __init__(self, impl='c_svc', kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
+ cache_size=100.0, eps=1e-3, C=1.0, nr_weight=0,
+ nu=0.5, p=0.1, shrinking=True, probability=False):
BaseLibsvm.__init__(self, impl, kernel, degree, gamma, coef0,
cache_size, eps, C, nr_weight, nu, p,
shrinking, probability)
-
@property
def coef_(self):
- if _kernel_types[self.kernel] != 'linear':
+ if self._kernel_types[self.kernel] != 'linear':
raise NotImplementedError('coef_ is only available when using a linear kernel')
- if self.support_.size == 0:
- raise Exception, 'No support vector'
-
- coef_ = []
- for i in range(self.dual_coef_.shape[0]):
- coef_.append(np.dot(self.dual_coef_[i], self.support_))
- coef_ = np.array(coef_)
- return coef_
+ return np.dot(self.dual_coef_, self.support_)
class SVR(BaseLibsvm):
"""
@@ -227,28 +255,13 @@ class SVR(BaseLibsvm):
--------
SVC
"""
- def __init__(self, impl='epsilon_svr', kernel='rbf', degree=3,
- gamma=0.0, coef0=0.0, cache_size=100.0, eps=1e-3,
- C=1.0, nr_weight=0, nu=0.5, p=0.1, shrinking=True,
- probability=False):
- BaseLibsvm.__init__(self, impl, kernel, degree, gamma, coef0,
+ def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
+ cache_size=100.0, eps=1e-3, C=1.0, nr_weight=0,
+ nu=0.5, p=0.1, shrinking=True, probability=False):
+ BaseLibsvm.__init__(self, 'epsilon_svr', kernel, degree, gamma, coef0,
cache_size, eps, C, nr_weight, nu, p,
shrinking, probability)
- @property
- def coef_(self):
- if _kernel_types[self.kernel] != 'linear':
- raise NotImplementedError(
- 'coef_ is only available when using a linear kernel')
- if self.support_.size == 0:
- raise Exception, 'No support vector'
-
- coef_ = []
- for i in range(self.dual_coef_.shape[0]):
- coef_.append(np.dot(self.dual_coef_[i], self.support_))
- coef_ = np.array(coef_)
- return coef_
-
class OneClassSVM(BaseLibsvm):
"""
Outlayer detection
@@ -261,12 +274,10 @@ class OneClassSVM(BaseLibsvm):
predict(X) : array
Predict using the model.
"""
- def __init__(self, kernel='rbf', degree=3,
- gamma=0.0, coef0=0.0, cache_size=100.0, eps=1e-3,
- C=1.0, nr_weight=0, nu=0.5, p=0.1, shrinking=True,
- probability=False):
- impl = 'one_class'
- BaseLibsvm.__init__(self, impl, kernel, degree, gamma, coef0,
+ def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
+ cache_size=100.0, eps=1e-3, C=1.0, nr_weight=0,
+ nu=0.5, p=0.1, shrinking=True, probability=False):
+ BaseLibsvm.__init__(self, 'one_class', kernel, degree, gamma, coef0,
cache_size, eps, C, nr_weight, nu, p,
shrinking, probability)
@@ -311,33 +322,36 @@ class LinearSVC(object):
http://www.csie.ntu.edu.tw/~cjlin/liblinear/
"""
- _solver_type = {'l2l2_1': 1, 'l2l2_0' : 2, 'l2l1_1' : 3, 'l1l2_0' : 5}
-
- def __init__(self, penalty='l2', loss='l2', dual=True, eps=1e-4, C=1.0):
- s = penalty + loss + '_' + str(int(dual))
- print s
- try: self.solver_type = self._solver_type[s]
- except KeyError:
- raise ValueError('Not supported set of arguments')
- self.eps = eps
- self.C = C
- _penalties = {'l2': 0, 'l1' : 6}
_weight_label = np.empty(0, dtype=np.int32)
_weight = np.empty(0, dtype=np.float64)
+ _solver_type_dict = {
+ 'PL2_LL2_D1' : 1, # L2 penalty, L2 loss, dual problem
+ 'PL2_LL2_D0' : 2, # L2 penalty, L2 loss, primal problem
+ 'PL2_LL1_D1' : 3, # L2 penalty, L1 Loss, dual problem
+ 'PL1_LL2_D0' : 5, # L2 penalty, L1 Loss, primal problem
+ }
+
+ def __init__(self, penalty='L2', loss='L2', dual=True, eps=1e-4, C=1.0):
+ self.solver_type = 'P' + penalty + '_L' + loss + '_D' + str(int(dual))
+ if not (self.solver_type in self._solver_type_dict.keys()):
+ raise ValueError('Not supported set of arguments: ' + self.solver_type)
+ self.eps = eps
+ self.C = C
+
def fit(self, X, Y):
X = np.asanyarray(X, dtype=np.float64, order='C')
Y = np.asanyarray(Y, dtype=np.int32, order='C')
- self.coef_with_intercept_, self.label_, self.bias_ = liblinear.train_wrap(X,
- Y, self.solver_type, self.eps, 1.0,
- self.C, 0,
- self._weight_label,
- self._weight)
+ self.raw_coef, self.label_, self.bias_ = \
+ liblinear.train_wrap(X, Y,
+ self._solver_type_dict[self.solver_type],
+ self.eps, 1.0, self.C, 0, self._weight_label,
+ self._weight)
def predict(self, T):
T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
- return liblinear.predict_wrap(T, self.coef_with_intercept_, self.solver_type,
+ return liblinear.predict_wrap(T, self.raw_coef, self._solver_type_dict[self.solver_type],
self.eps, self.C,
self._weight_label,
self._weight, self.label_,
@@ -348,9 +362,9 @@ class LinearSVC(object):
@property
def intercept_(self):
- return self.coef_with_intercept_[:,-1]
+ return self.raw_coef[:,-1]
@property
def coef_(self):
- return self.coef_with_intercept_[:,:-1]
+ return self.raw_coef[:,:-1]
diff --git a/scikits/learn/tests/test_svm.py b/scikits/learn/tests/test_svm.py
index c6a694be51ca675e998dc3f2ee61d71ea9295065..976d6844249caf1ba560b9418788a781113c4657 100644
--- a/scikits/learn/tests/test_svm.py
+++ b/scikits/learn/tests/test_svm.py
@@ -4,30 +4,79 @@ from numpy.testing import assert_array_equal, \
assert_array_almost_equal, \
assert_raises
-# Data is just 6 separable points in the plane
-X = np.array( [[-2,-1], [-1, -1], [-1, -2], [1,1], [1,2], [2, 1]])
-Y = np.array( [1, 1, 1, 2, 2, 2])
-T = np.array( [[-1,-1], [2, 2], [3, 2]] )
+# test sample 1
+X = [[-2,-1], [-1, -1], [-1, -2], [1,1], [1,2], [2, 1]]
+Y = [1, 1, 1, 2, 2, 2]
+T = [[-1,-1], [2, 2], [3, 2]]
true_result = [1, 2, 2]
-def test_svm_params():
+# test sample 2
+X2 = [[0, 0, 0], [1, 1, 1], [2, 0, 0, ],
+ [0, 0, 2], [3, 3, 3]]
+Y2 = [1, 2, 2, 2, 3]
+T2 = [[-1, -1, -1], [1, 1, 1], [2, 2, 2]]
+true_result2 = [1, 2, 3]
+
+def test_CSVC():
"""
C_SVC algorithm and linear kernel.
- This checks that we retrieve the correct parameters.
+ We test this on two datasets, the first one with two classes and
+ the second one with three classes. We check for predicted values
+ and estimated parameters.
+
+ TODO: check with different parameters of C
"""
- clf = svm.SVC(kernel='linear')
+ clf = svm.SVC(kernel='linear')
clf.fit(X, Y)
-
+ pred = clf.predict(T)
assert_array_equal(clf.dual_coef_, [[ 0.25, -.25]])
assert_array_equal(clf.support_, [[-1,-1], [1, 1]])
- assert_array_equal(clf.rho_, [0.])
+ assert_array_equal(clf.intercept_, [0.])
+ assert_array_equal(pred, true_result)
+
+ clf.fit(X2, Y2)
+ pred = clf.predict(T2)
+ assert_array_almost_equal(clf.dual_coef_,
+ [[ .99, -.006, -.49, -.49, -.07],
+ [ .072, .16, 0, 0, -.16]], decimal=2)
+ assert_array_equal(clf.support_,
+ [[ 0., 0., 0.],
+ [ 1., 1., 1.],
+ [ 2., 0., 0.],
+ [ 0., 0., 2.],
+ [ 3., 3., 3.]])
+ assert_array_equal(pred, true_result2)
+
+
+def test_SVR():
+ """
+ Test SVM regression
+ TODO: simplify this. btw, is it correct ?
+ """
+
+ clf = svm.SVR()
+ clf.fit(X, Y)
+ pred = clf.predict(T)
+
+ assert_array_almost_equal(clf.dual_coef_,
+ [[-0.01441007, -0.51530605, -0.01365979,
+ 0.51569493, 0.01387495, 0.01380604]])
+ print clf.support_
+ assert_array_almost_equal(clf.support_, X)
+ assert_array_almost_equal(pred,[ 1.10001274, 1.86682485, 1.73300377])
+
+
+def test_oneclass():
+ """
+ FIXME: this does nothing
+ """
+ clf = svm.OneClassSVM()
+ clf.fit(X, Y)
+ assert_array_equal(Y, [1, 1, 1, 2, 2, 2])
+
-def test_fit():
- clf = svm.SVC()
- clf.fit([[1,2]], [0])
- assert_array_equal(clf.predict([[-1, -1]]), [0.])
def test_tweak_params():
"""
@@ -47,94 +96,58 @@ def test_tweak_params():
clf.dual_coef_ = np.array([[.0, 1.]])
assert_array_equal(clf.predict([[-.1, -.1]]), [2])
-def test_error():
- """
- Test that it gives proper exception on deficient input
- """
- # impossible value of nu
- clf = svm.SVC(impl='nu_svc', kernel='linear', nu=0.0)
- assert_raises(ValueError, clf.fit, X, Y)
-
- Y2 = Y[:-1] # wrong dimensions for labels
- assert_raises(ValueError, svm.SVC, X, Y2)
-
-def test_predict():
- clf = svm.SVC()
- clf.fit(X, Y)
- assert_array_equal(clf.predict(T), true_result)
-
def test_probability():
"""
- predict probabilities
+ predict probabilities.
+ FIXME: is it harmless that we obtain slightly different results on
+ different operating systems ? (that is why we only check for 1
+ decimal precission)
+ TODO: test also on an example with intercept != 0
"""
clf = svm.SVC(probability=True)
clf.fit(X, Y)
assert_array_almost_equal(clf.predict_proba(T),
[[ 0.81936054, 0.18063946],
[ 0.18923853, 0.81076147],
- [ 0.27698362, 0.72301638]])
+ [ 0.27698362, 0.72301638]],
+ decimal=1)
-def test_noncontiguous():
+def test_error():
"""
- Test with arrays that are non-contiguous.
+ Test that it gives proper exception on deficient input
"""
- Xt = X.transpose()
+ # impossible value of nu
+ clf = svm.SVC(impl='nu_svc', kernel='linear', nu=0.0)
+ assert_raises(ValueError, clf.fit, X, Y)
+
+ Y2 = Y[:-1] # wrong dimensions for labels
+ assert_raises(ValueError, svm.SVC, X, Y2)
+
+ # Test with arrays that are non-contiguous.
+ Xt = np.array(X).transpose()
Yt = [1, 2]
clf = svm.SVC()
clf.fit(Xt, Yt)
assert_array_equal(clf.predict(T), [1, 2, 2])
-def test_predict_multiclass():
- """
- this example is from libsvm/README
- """
- X = [[0, 0.1, 0.2, 0, 0],
- [ 0, 0.1, 0.3, -1.2, 0],
- [0.4, 0, 0, 0, 0],
- [0, 0.1, 0, 1.4, 0.5],
- [-0.1,-0.2, 0.1, 1.1, 0.1]]
- Y = [1,2,1,2,3]
- test = [[0, 1, 0, -1, 0]]
- clf = svm.SVC()
- clf.fit(X, Y)
- result = clf.predict(test)
- assert_array_equal(result, [2])
-
-def test_regression():
- """
- Test SVM regression
- TODO: simplify this. btw, is it correct ?
- """
- clf = svm.SVR()
- clf.fit([[0,0], [1, 1]], [0, 1])
- assert_array_almost_equal(clf.predict([[0,0], [1, 1]]), [.099999, .9])
-
-def test_oneclass():
- """
- FIXME: this does nothing
- """
- clf = svm.OneClassSVM()
- clf.fit(X, Y)
- assert_array_equal(Y, [1, 1, 1, 2, 2, 2])
-
def test_LinearSVC():
clf = svm.LinearSVC()
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# the same with l1 penalty
- clf = svm.LinearSVC(penalty='l1', dual=False)
+ clf = svm.LinearSVC(penalty='L1', dual=False)
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# l2 penalty with dual formulation
- clf = svm.LinearSVC(penalty='l2', dual=True)
+ clf = svm.LinearSVC(penalty='L2', dual=True)
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
#
- clf = svm.LinearSVC(penalty='l2', loss='l1', dual=True)
+ clf = svm.LinearSVC(penalty='L2', loss='L1', dual=True)
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
@@ -144,11 +157,11 @@ def test_coef_and_intercept_SVC_vs_LinearSVC():
"""
svc = svm.SVC(kernel='linear', C=1)
svc.fit(X, Y)
- linsvc = svm.LinearSVC(C=1, penalty='l2', loss='l1', dual=True)
+ linsvc = svm.LinearSVC(C=1, penalty='L2', loss='L1', dual=True)
linsvc.fit(X, Y)
assert_array_equal(linsvc.coef_.shape, svc.coef_.shape)
assert_array_almost_equal(linsvc.coef_, svc.coef_, 5)
assert_array_almost_equal(linsvc.intercept_, svc.intercept_, 6)
-
\ No newline at end of file
+