def main():
# Load dataset
data = load_iris()
X = data['data']
y = data['target']
# Reduce to two classes
X = X[y != 0]
y = y[y != 0]
y -= 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = LogisticRegression()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)
Plot().plot_in_2d(X,
y,
title="Logistic Regression",
accuracy=accuracy,
legend_labels=data['target_names'])
def main():
data = load_iris()
X = normalize(data['data'])
y = data['target']
# One-hot
y = np.zeros((data['target'].shape[0], 3))
y[np.arange(data['target'].shape[0]).astype('int'), data['target'].astype('int')] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = MLP()
clf.fit(X_train, y_train, n_epochs=1000, lr=0.01, n_units=16)
y_pred = np.argmax(clf.predict(X_test), axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = np.mean(y_pred == y_test)
y = np.argmax(y, axis=1)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X, y,
title="Multilayer Perceptron",
accuracy=accuracy,
legend_labels=data['target_names'])
def main():
# Load dataset
data = load_iris()
X = data['data']
y = data['target']
# Reduce to two classes
X = X[y != 0]
y = y[y != 0]
y -= 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = LogisticRegression()
clf.fit(X_train, y_train)
y_pred = np.rint(clf.predict(X_test))
accuracy = np.mean(y_pred == y_test)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X, y,
title="Logistic Regression",
accuracy=accuracy,
legend_labels=data['target_names'])
def main():
data = load_iris()
X = normalize(data['data'])
y = data['target']
# One-hot
y = np.zeros((data['target'].shape[0], 3))
y[np.arange(data['target'].shape[0]).astype('int'),
data['target'].astype('int')] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = MLP()
clf.fit(X_train, y_train, n_epochs=1000, lr=0.01, n_units=16)
y_pred = np.argmax(clf.predict(X_test), axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = np.mean(y_pred == y_test)
y = np.argmax(y, axis=1)
print("Accuracy:", accuracy)
Plot().plot_in_2d(X,
y,
title="Multilayer Perceptron",
accuracy=accuracy,
legend_labels=data['target_names'])
def main():
data = load_regression()
X = data['data'][:-150]
y = data['target'][:-150]
# Insert constant ones for bias
X = np.insert(X, 0, 1, axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
n_samples, n_features = np.shape(X)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = (np.mean(y_test - y_pred)**2)
# Print MSE
print('Mean Squared Error: %.4f' % mse)
y_pred_line = model.predict(X)
X = X[:, 1]
X_train = X_train[:, 1]
X_test = X_test[:, 1]
# Color map
cmap = plt.get_cmap('viridis')
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
plt.plot(366 * X,
y_pred_line,
color='black',
linewidth=2,
label="Prediction")
plt.suptitle("Linear Regression")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
plt.show()
def main():
data = load_iris()
X = data['data']
y = data['target'].astype('int')
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = KNN()
y_pred = clf.predict(5, X_test, X_train, y_train)
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)
Plot().plot_in_2d(X,
y,
title="K-Nearest Neighbors",
accuracy=accuracy,
legend_labels=data['target_names'])
def main():
data = load_regression()
X = data['data'][:-150]
y = data['target'][:-150]
# Insert constant ones for bias
X = np.insert(X, 0, 1, axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
n_samples, n_features = np.shape(X)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = (np.mean(y_test - y_pred)**2)
# Print MSE
print ('Mean Squared Error: %.4f' % mse)
y_pred_line = model.predict(X)
X = X[:, 1]
X_train = X_train[:, 1]
X_test = X_test[:, 1]
# Color map
cmap = plt.get_cmap('viridis')
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction")
plt.suptitle("Linear Regression")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
plt.show()
def main():
data = load_iris()
X = data['data']
y = data['target']
# Reduce to two classes
X = X[y != 2]
y = y[y != 2]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = LDA()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X, y,
title="Linear Discriminant Analysis",
accuracy=accuracy,
legend_labels=data['target_names'])