def pca_faces():
data = sio.loadmat(
"ml_course_material/machine-learning-ex7/ex7/ex7faces.mat")
X = data["X"]
plt.figure(0)
plot_images(X)
plt.show(block=False) #somehow when plotting, svd will not converge
normalizer = feature.FeatureNormalizer(X)
X_norm = normalizer.normalized_x_m
while True:
try:
(U, S) = pca.pca(X_norm)
break
except:
pass
plt.figure(1)
plot_images(U.T)
plt.show(block=False)
k = 100
Z = pca.project(X, U, k)
X_approx = pca.reconstruct(Z, U, k)
plt.figure(2)
plot_images(X_approx)
plt.show()
def pca_warmup():
data = sio.loadmat(
"ml_course_material/machine-learning-ex7/ex7/ex7data1.mat")
X = data["X"]
# plt.plot(X[:, 0], X[:, 1], '.')
# plt.show(block=False)
normalizer = feature.FeatureNormalizer(X)
X_norm = normalizer.normalized_x_m
(U, S) = pca.pca(X_norm)
print(U[:, 0])
plt.plot(X_norm[:, 0], X_norm[:, 1], '.')
plt.show(block=False)
plt.plot([-3, U[0, 0]], [-3, U[0, 1]], "x-")
plt.show(block=False)
Z = pca.project(X_norm, U, 1)
print(Z)
X_approx = pca.reconstruct(Z, U, 1)
print(X_approx)
plt.plot(X_approx[:, 0], X_approx[:, 1], "y.")
plt.show()
def learning_curves_of_different_polynomial_degree(X_train, y_train, X_cv,
y_cv, estimator_predictor,
cost_fun,
max_polynomial_degree):
"""
Trains theta with X_train and y_train using minimize_fun with different polynomial degree
(1 to provided max_polynomial_degree). Calculates and return training set and cross-validation error.
:param X_train:
:param y_train:
:param X_cv:
:param y_cv:
:param estimator_predictor: has methods fit(X, y) and predict(X) -> y
:param cost_fun: expected signature cost_fun(y_true, y_pred) -> float
:param max_polynomial_degree:
:return: training errors, cross-validation errors
"""
j_train = []
j_cv = []
for polynomial_degree in range(1, max_polynomial_degree):
poly_features = PolynomialFeatures(polynomial_degree,
include_bias=False)
X_train_poly = poly_features.fit_transform(X_train)
normalizer = feature.FeatureNormalizer(X_train_poly)
X_train_poly = normalizer.normalize_matrix(X_train_poly)
X_train_poly = np.hstack((np.ones(
(X_train_poly.shape[0], 1)), X_train_poly))
estimator_predictor.fit(X_train_poly, y_train)
j_train.append(
cost_fun(y_train, estimator_predictor.predict(X_train_poly)))
X_cv_poly = poly_features.fit_transform(X_cv)
X_cv_poly = normalizer.normalize_matrix(X_cv_poly)
X_cv_poly = np.hstack((np.ones((X_cv_poly.shape[0], 1)), X_cv_poly))
j_cv.append(cost_fun(y_cv, estimator_predictor.predict(X_cv_poly)))
plt.plot(list(range(1, len(j_train) + 1)), j_train, label="j_train")
plt.plot(list(range(1, len(j_train) + 1)), j_cv, label="j_cv")
plt.xlabel("polynomial degree")
plt.ylabel("error")
plt.legend()
plt.show()
return j_train, j_cv
def pca_on_bird():
bird_image = image.imread(
"ml_course_material/machine-learning-ex7/ex7/bird_small.png")
plt.imshow(bird_image)
plt.show(block=False)
im_shape = bird_image.shape
X = bird_image.reshape([im_shape[0] * im_shape[1], 3])
k = 16
(centroids,
closest_centroids) = k_means.k_means(X,
k_means.init_random_centroids(X, k))
nr_samples = 1000
rng = np.random.default_rng()
random_indices = rng.integers(0, X.shape[0], nr_samples)
colors = []
X_rand = np.empty((nr_samples, X.shape[1]))
for i in range(nr_samples):
colors.append(closest_centroids[random_indices[i]])
X_rand[i, :] = X[random_indices[i], :]
plt.hsv()
fig = plt.figure(3)
ax = fig.gca(projection='3d')
ax.scatter(X_rand[:, 0], X_rand[:, 1], X_rand[:, 2], c=colors)
plt.show(block=False)
X_norm = feature.FeatureNormalizer(X).normalized_x_m
while True:
try:
(U, S) = pca.pca(X_norm)
break
except:
pass
Z = pca.project(X, U, 2)
Z_rand = np.empty((nr_samples, Z.shape[1]))
for i in range(nr_samples):
Z_rand[i, :] = Z[random_indices[i], :]
plt.figure(4)
plt.scatter(Z_rand[:, 0], Z_rand[:, 1], c=colors)
plt.show()
def anomaly_detection_high_dimensional_dataset():
data = sio.loadmat("ml_course_material/machine-learning-ex8/ex8/ex8data2.mat")
X = data["X"]
X_cv = data["Xval"]
y_cv = data["yval"]
(mu, sigma2) = estimate_gaussian(X)
p_cv = gaussian(X_cv, mu, sigma2)
(epsilon, f1) = select_threshold(p_cv, y_cv)
print(f"best epsilon is {epsilon} with F1 score {f1}")
p = gaussian(X, mu, sigma2)
(anomalies_indices_x, anomalies_indices_y) = np.nonzero(p < epsilon)
print(f"{len(anomalies_indices_x)} anomalies")
# pca for fun
normnalizer = feature.FeatureNormalizer(X)
X_norm = normnalizer.normalized_x_m
(U, S) = pca.pca(X_norm)
Z = pca.project(X_norm, U, 2)
visualize(Z)
plt.plot(Z[anomalies_indices_x, 0], Z[anomalies_indices_x, 1], '+')
plt.show()
return ((diff.T @ diff) / (2 * m))[0][0]
plt.figure(1)
(j_train, j_cv) = lc.learning_curves_of_different_training_set_size(
X_train, y_train, X_cv, y_cv, LinearRegressionEstimatorPredictor(), cost)
plt.figure(2)
(j_train, j_cv) = lc.learning_curves_of_different_polynomial_degree(
X_train, y_train, X_cv, y_cv, LinearRegressionEstimatorPredictor(), cost,
12)
poly_features = PolynomialFeatures(8, include_bias=False)
X_train_poly = poly_features.fit_transform(X_train)
normalizer = feature.FeatureNormalizer(X_train_poly)
X_train_poly = normalizer.normalize_matrix(X_train_poly)
X_cv_poly = poly_features.fit_transform(X_cv)
X_cv_poly = normalizer.normalize_matrix(X_cv_poly)
X_test_poly = poly_features.fit_transform(X_test)
X_test_poly = normalizer.normalize_matrix(X_test_poly)
regularization_lambda = 1
result = op.minimize(fun=lire.linear_regression_cost_gradient,
x0=np.zeros(X_train_poly.shape[1] + 1),
args=(np.hstack((np.ones(
(X_train_poly.shape[0], 1)), X_train_poly)), y_train,
regularization_lambda),
method="CG",
jac=True,
import ml.feature as ft
import ml.logistic_regression as lore
import ml.predict as predict
data = np.loadtxt("ml_course_material/machine-learning-ex2/ex2/ex2data1.txt", delimiter=",")
(m, feature_length_with_result) = data.shape
x = data[:, 0:feature_length_with_result - 1].reshape((m, feature_length_with_result - 1))
y = data[:, feature_length_with_result - 1].reshape((m, 1))
success_indices = np.nonzero(y.flatten() == 1)
fail_indices = np.nonzero(y.flatten() == 0)
normalizer = ft.FeatureNormalizer(x)
normalized_x_m = np.hstack((np.ones((m, 1)), normalizer.normalized_x_m))
# (theta, costs) = ml.gradient_descent(normalized_x_m, y, ml.logistic_regression_cost,
# ml.logistic_regression_cost_derivative, alpha=0.01, num_iter=10000)
(theta, num_of_evaluations, return_code) = op.fmin_tnc(func=lore.logistic_regression_cost_gradient, x0=np.zeros((3, 1)),
args=(np.hstack((np.ones((m, 1)), x)), y))
theta = theta.reshape(len(theta), 1)
plt.figure(0)
plt.subplot(211)
plt.plot(x[success_indices, 0], x[success_indices, 1], "kx")
plt.plot(x[fail_indices, 0], x[fail_indices, 1], "yo")
plt.plot()
