def bench_scikit(X, Y, T):
"""
bench with scikit-learn bindings on libsvm
"""
import scikits.learn
from scikits.learn.svm import SVC
gc.collect()
# start time
tstart = datetime.now()
clf = SVC(kernel='linear');
clf.fit(X, Y);
Z = clf.predict(T)
delta = (datetime.now() - tstart)
# stop time
scikit_results.append(delta.seconds + delta.microseconds/mu_second)
def test_SVMModelField():
X = [[0 ,0],[1, 1]]
y = [0, 1]
svm = SVM()
clf = SVC()
clf.fit(X,y)
a1 = clf.predict([[2.,2.]])
#print clf
#print a1
svm.classifier = clf
svm.save(safe=True)
s = SVM.objects.first()
#print s.classifier
a2 = s.classifier.predict([[2., 2.]])
#print a2
assert a1 == a2
# project the input data on the eigenfaces orthonormal basis
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
################################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
clf = SVC(C=100).fit(X_train_pca, y_train, class_weight="auto")
################################################################################
# Quantitative evaluation of the model quality on the test set
y_pred = clf.predict(X_test_pca)
print classification_report(y_test, y_pred, labels=selected_target,
class_names=category_names[selected_target])
print confusion_matrix(y_test, y_pred, labels=selected_target)
################################################################################
# Qualitative evaluation of the predictions using matplotlib
n_row = 3
n_col = 4
pl.figure(figsize=(2*n_col, 2.3*n_row))
pl.subplots_adjust(bottom=0, left=.01, right=.99, top=.95, hspace=.15)
for i in range(n_row * n_col):
import numpy as np X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1, 2, 2]) from scikits.learn.svm import SVC clf = SVC() clf.fit(X, y) print clf.predict([[-0.8, -1]])
