def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
n_samples, n_features = np.shape(X)
n_hidden, n_output = 50, 10
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
optimizer = GradientDescent_(learning_rate=0.0001, momentum=0.3)
# MLP
clf = MultilayerPerceptron(n_iterations=6000,
batch_size=200,
optimizer=optimizer,
val_error=True)
clf.add(DenseLayer(n_inputs=n_features, n_units=n_hidden))
clf.add(DenseLayer(n_inputs=n_hidden, n_units=n_hidden))
clf.add(DenseLayer(n_inputs=n_hidden, n_units=n_output, activation_function=Softmax))
clf.fit(X_train, y_train)
clf.plot_errors()
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
# MLP
clf = MultilayerPerceptron(n_hidden=12,
n_iterations=1000,
learning_rate=0.0001,
momentum=0.8,
activation_function=ExpLU,
early_stopping=True,
plot_errors=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
def main():
print("-- XGBoost --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=2)
clf = XGBoost(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="XGBoost",
accuracy=accuracy,
legend_labels=data.target_names)
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
seed=1)
# Optimization method for finding weights that minimizes loss
optimizer = RMSprop(learning_rate=0.01)
# Perceptron
clf = Perceptron(n_iterations=5000,
activation_function=ExpLU,
optimizer=optimizer,
early_stopping=True,
plot_errors=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="Perceptron",
accuracy=accuracy,
legend_labels=np.unique(y))
def main():
data = datasets.load_digits()
X = data.data
y = data.target
digit1 = 1
digit2 = 8
idx = np.append(np.where(y == digit1)[0], np.where(y == digit2)[0])
y = data.target[idx]
# Change labels to {-1, 1}
y[y == digit1] = -1
y[y == digit2] = 1
X = data.data[idx]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Adaboost classification
clf = Adaboost(n_clf=5)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimensions to 2d using pca and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Adaboost", accuracy=accuracy)
def main():
# Load the dataset
X, y = datasets.make_blobs()
# Cluster the data
clf = GaussianMixtureModel(k=3)
y_pred = clf.predict(X)
pca = PCA()
pca.plot_in_2d(X, y_pred, title="GMM Clustering")
pca.plot_in_2d(X, y, title="Actual Clustering")
def main():
# Load the dataset
X, y = datasets.make_moons(n_samples=300, noise=0.1, shuffle=False)
# Cluster the data using DBSCAN
clf = DBSCAN(eps=0.17, min_samples=5)
y_pred = clf.predict(X)
# Project the data onto the 2 primary principal components
pca = PCA()
pca.plot_in_2d(X, y_pred, title="DBSCAN")
pca.plot_in_2d(X, y, title="Actual Clustering")
def main():
# Load the dataset
X, y = datasets.make_blobs()
# Cluster the data using K-Medoids
clf = PAM(k=3)
y_pred = clf.predict(X)
# Project the data onto the 2 primary principal components
pca = PCA()
pca.plot_in_2d(X, y_pred, title="PAM Clustering")
pca.plot_in_2d(X, y, title="Actual Clustering")
def main():
print("-- Classification Tree --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = ClassificationTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="Decision Tree",
accuracy=accuracy,
legend_labels=data.target_names)
print("-- Regression Tree --")
X, y = datasets.make_regression(n_features=1,
n_samples=100,
bias=0,
noise=5)
X_train, X_test, y_train, y_test = train_test_split(standardize(X),
y,
test_size=0.3)
clf = RegressionTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
# Plot the results
plt.scatter(X_test[:, 0], y_test, color='black')
plt.scatter(X_test[:, 0], y_pred, color='green')
plt.title("Regression Tree (%.2f MSE)" % mse)
plt.show()
def main():
data = datasets.load_iris()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
clf = NaiveBayes()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Naive Bayes", accuracy=accuracy, legend_labels=data.target_names)
def main():
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = -1
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = SupportVectorMachine(kernel=polynomial_kernel, power=4, coef=1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Support Vector Machine", accuracy=accuracy)
def main():
data = datasets.load_iris()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = KNN(k=3)
y_pred = clf.predict(X_test, X_train, y_train)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimensions to 2d using pca and plot the results
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="K Nearest Neighbors",
accuracy=accuracy,
legend_labels=data.target_names)
def main():
# Load dataset
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = 0
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
clf = LogisticRegression(gradient_descent=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Logistic Regression", accuracy=accuracy)