print("// Обучение начато //")
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
print("// Обучение завершено //")
# Логистическая регрессия
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
lr = LogisticRegression(C=100.0, random_state=1)
lr.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std,
y_combined,
classifier=lr,
test_idx=range(105, 150))
plt.xlabel('Длина лепестка [std]')
plt.ylabel('Ширина лепестка [std]')
plt.legend(loc='upper left')
plt.show()
# Вероятности
lr.predict_proba(X_test_std[:3, :])
# Настройка переобучения
# weights, params = [], []
# for c in np.arange(-5, 5):
# lr = LogisticRegression(C=10.**c, random_state=1)
from sklearn import datasets
from sklearn.model_selection import train_test_split
from LogisticRegressionGD.MyLogisticRegressionGD import LogisticRegressionGD
from Algorithms.plotDecisionRegions import plot_decision_regions
import matplotlib.pyplot as plt
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=1,
stratify=y)
X_train_01_subset = X_train[(y_train == 0) | y_train == 1]
y_train_01_subset = y_train[(y_train == 0) | y_train == 1]
lrgd = LogisticRegressionGD(eta=0.05, n_iter=1000, random_state=1)
lrgd.fit(X_train_01_subset, y_train_01_subset)
plot_decision_regions(X=X_train_01_subset,
y=y_train_01_subset,
classifier=lrgd)
plt.xlabel('Длина лепестка [std]')
plt.ylabel('Ширина лепестка [std]')
plt.legend(loc='upper left')
plt.show()
from sklearn.svm import SVC
from Algorithms.plotDecisionRegions import plot_decision_regions
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
X_xor = np.random.randn(200, 2)
y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0)
y_xor = np.where(y_xor, 1, -1)
# plt.scatter(X_xor[y_xor == 1, 0],
# X_xor[y_xor == 1, 1],
# c='b', marker='x',
# label='1')
# plt.scatter(X_xor[y_xor == -1, 0],
# X_xor[y_xor == -1, 1],
# c='r', marker='s',
# label='-1')
# plt.xlim([-3, 3])
# plt.ylim([-3, 3])
# plt.legend(loc='best')
# plt.show()
svm = SVC(kernel='rbf', random_state=1, gamma=0.10, C=10.0)
svm.fit(X_xor, y_xor)
plot_decision_regions(X_xor, y_xor, classifier=svm, test_idx=range(105, 150))
plt.xlabel('Длина лепестка [std]')
plt.ylabel('Ширина лепестка [std]')
plt.legend(loc='upper left')
plt.show()