def main():
#read in data, parse into training and target sets
data = csv_io.read_data("./filtered_classes_musiconly.csv")
target = np.array( [x[0] for x in data] )
train = np.array( [x[1:] for x in data] )
train_scaled = preprocessing.scale(train)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(train_scaled, target, test_size = 0.8)
clf = SVC(kernel='rbf', C = 1000.0, gamma=0.001).fit(X_train, y_train)
y_val_predict = clf.predict(X_test)
print metrics.zero_one_score(y_test, y_val_predict)
def main():
#read in data, parse into training and target sets
data = csv_io.read_data("./filtered_classes.csv")
o_target = np.array( [x[0] for x in data] )
o_train = np.array( [x[1:] for x in data] )
for i in range(0, 100):
#Split the data randomly into 80% training and 20% test
X_train, X_test, y_train, y_test = cross_validation.train_test_split(o_train,o_target, test_size = 0.20)
#Compute the most frequent class in the training set
H = histogram(y_train)
mc = max(H.iteritems(), key=operator.itemgetter(1))[0]
y_predict = np.empty(len(y_test))
y_predict[:] = mc
#print metrics.classification_report(y_test, y_predict)
print i, str(metrics.zero_one_score(y_test, y_predict))