diff --git a/scikits/learn/svm.py b/scikits/learn/svm.py index a4c3d19c9d282390fa9f04476fc136ca2ac708ae..114524b45ebfae1b6bc2aef0a64347b71084b046 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,