def classify_perceptron():
print "perceptron"
(X_train, y_train), (X_test, y_test) = util.load_all_feat()
print "original X_train shape", X_train.shape
clf = Perceptron()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
print "accuracy score:", accuracy_score(y_test, pred)
def classifiy_svm():
print "SVM"
(X_train, y_train), (X_test, y_test) = util.load_all_feat()
print "original X_train shape", X_train.shape
clf = LinearSVC()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
print "accuracy score:", accuracy_score(y_test, pred)
def classify_sgd(loss="hinge"):
print "SGD Clasifier with loss function({})".format(loss)
(X_train, y_train), (X_test, y_test) = util.load_all_feat()
X_train = X_train[:, :4096]
X_test = X_test[:, :4096]
clf = SGDClassifier(loss=loss, n_jobs=-1)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
print "accuracy score:", accuracy_score(y_test, pred)
nclass = len(clf.coef_)
def classify_logistic():
print "logistic regression"
(X_train, y_train), (X_test, y_test) = util.load_all_feat()
print "original X_train shape", X_train.shape
clf = RandomizedLogisticRegression(n_jobs=2)
clf.fit(X_train, y_train)
# clf = LogisticRegression()
# clf.fit(X_train, y_train)
pred = clf.predict(X_test)
print "accuracy score:", accuracy_score(y_test, pred)
import ipdb; ipdb.set_trace() # XXX BREAKPOINT