From 027de8f9685ce9ab2481314f4db67342f24b3a0e Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Wed, 15 Dec 2010 09:11:33 +0100
Subject: [PATCH] FIX: backwards compatibility for scipy <= 0.8
ndimage.measurement.sum does not accept int64 on these versions.
---
scikits/learn/lda.py | 14 +++++++++-----
scikits/learn/qda.py | 14 +++++++++-----
2 files changed, 18 insertions(+), 10 deletions(-)
diff --git a/scikits/learn/lda.py b/scikits/learn/lda.py
index 01caf0e1be..ebd189a3f8 100644
--- a/scikits/learn/lda.py
+++ b/scikits/learn/lda.py
@@ -84,10 +84,14 @@ class LDA(BaseEstimator, ClassifierMixin):
self._set_params(**params)
X = np.asanyarray(X)
y = np.asanyarray(y)
- if y.dtype.char.lower() not in ('i', 'l'):
+ if y.dtype.char.lower() not in ('b', 'h', 'i'):
# We need integer values to be able to use
- # ndimage.measurements and np.bincount on numpy > 2.0
- y = y.astype(np.int)
+ # ndimage.measurements and np.bincount on numpy >= 2.0.
+ # We currently support (u)int8, (u)int16 and (u)int32.
+ # Note that versions of scipy >= 0.8 can also accept
+ # (u)int64. We however don't support it for backwards
+ # compatibility.
+ y = y.astype(np.int32)
if X.ndim != 2:
raise ValueError('X must be a 2D array')
if X.shape[0] != y.shape[0]:
@@ -102,8 +106,8 @@ class LDA(BaseEstimator, ClassifierMixin):
raise ValueError('y has less than 2 classes')
classes_indices = [(y == c).ravel() for c in classes]
if self.priors is None:
- counts = np.array(ndimage.measurements.sum(np.ones(len(y)),
- y, index=classes))
+ counts = np.array(ndimage.measurements.sum(
+ np.ones(n_samples, dtype=y.dtype), y, index=classes))
self.priors_ = counts / float(n_samples)
else:
self.priors_ = self.priors
diff --git a/scikits/learn/qda.py b/scikits/learn/qda.py
index 5cbeeb6537..40bdfe70e6 100644
--- a/scikits/learn/qda.py
+++ b/scikits/learn/qda.py
@@ -96,10 +96,14 @@ class QDA(BaseEstimator, ClassifierMixin):
raise ValueError(
'Incompatible shapes: X has %s samples, while y '
'has %s' % (X.shape[0], y.shape[0]))
- if y.dtype.char.lower() not in ('i', 'l'):
+ if y.dtype.char.lower() not in ('b', 'h', 'i'):
# We need integer values to be able to use
- # ndimage.measurements and np.bincount on numpy > 2.0
- y = y.astype(np.int)
+ # ndimage.measurements and np.bincount on numpy >= 2.0.
+ # We currently support (u)int8, (u)int16 and (u)int32.
+ # Note that versions of scipy >= 0.8 can also accept
+ # (u)int64. We however don't support it for backwards
+ # compatibility.
+ y = y.astype(np.int32)
n_samples, n_features = X.shape
classes = np.unique(y)
n_classes = classes.size
@@ -107,8 +111,8 @@ class QDA(BaseEstimator, ClassifierMixin):
raise exceptions.ValueError('y has less than 2 classes')
classes_indices = [(y == c).ravel() for c in classes]
if self.priors is None:
- counts = np.array(ndimage.measurements.sum(np.ones(len(y)),
- y, index=classes))
+ counts = np.array(ndimage.measurements.sum(
+ np.ones(n_samples, dtype=y.dtype), y, index=classes))
self.priors_ = counts / float(n_samples)
else:
self.priors_ = self.priors
--
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