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))