From 0bd5615c55399931021a91786987629945971adc Mon Sep 17 00:00:00 2001
From: Gael Varoquaux <gael.varoquaux@normalesup.org>
Date: Wed, 21 Apr 2010 16:02:40 +0000
Subject: [PATCH] ENH: Make it possible for SVM.predict to work on a single
sample.
git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@679 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
---
scikits/learn/svm.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/scikits/learn/svm.py b/scikits/learn/svm.py
index a4c3d19c9d..114524b45e 100644
--- a/scikits/learn/svm.py
+++ b/scikits/learn/svm.py
@@ -71,7 +71,7 @@ class BaseLibsvm(object):
return self
def predict(self, T):
- T = np.asanyarray(T, dtype=np.float64, order='C')
+ T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
return libsvm.predict_from_model_wrap(T, self.support_,
self.coef_, self.rho_, self.svm,
self.kernel, self.degree, self.gamma,
@@ -85,7 +85,7 @@ class BaseLibsvm(object):
def predict_proba(self, T):
if not self.probability:
raise ValueError("probability estimates must be enabled to use this method")
- T = np.asanyarray(T, dtype=np.float64, order='C')
+ T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
return libsvm.predict_prob_from_model_wrap(T, self.support_,
self.coef_, self.rho_, self.svm,
self.kernel, self.degree, self.gamma,
@@ -281,7 +281,7 @@ class LinearSVC(object):
self._weight)
def predict(self, T):
- T = np.asanyarray(T, dtype=np.float64, order='C')
+ T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
return liblinear.predict_wrap(T, self.coef_, self.solver_type,
self.eps, self.C,
self._weight_label,
--
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