"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data",
header=None)
X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.3, random_state=0)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.fit_transform(X_test)
#----------------------------------------------
lda = LDA(n_components=2)
X_train_lda = lda.fit_transform(X_train_std, y_train)
lr = LogisticRegression()
lr.fit(X_train_lda, y_train)
#plot_decision_regions(X_train_lda, y_train, classifier=lr)
#plt.xlabel('LD1')
#plt.ylabel('LD2')
#plt.legend(loc='lower left')
#plt.show()
X_test_lda = lda.fit_transform(X_test_std, y_test)
lr.fit(X_test_lda, y_test)
plot_decision_regions(X_test_lda, y_test, classifier=lr)
plt.xlabel('LD1')
plt.ylabel('LD2')
plt.legend(loc='lower left')
plt.show()
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=0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
X_combined = np.vstack((X_train, X_test))
#----------------------------------------------------------
forest = RandomForestClassifier(criterion="entropy",
n_estimators=10,
random_state=0,
n_jobs=2)
forest.fit(X_train, y_train)
plot_decision_regions(X_combined,
y_combined,
classifier=forest,
test_idx=range(105, 150))
plt.xlabel("petal length")
plt.ylabel("petal width")
plt.legend(loc="upper left")
plt.show()
df_wine = pd.read_csv(
"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data",
header=None)
X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.3, random_state=0)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.fit_transform(X_test)
#------------------------------------------------------------------------
pca = PCA(n_components=2, whiten=False)
lr = LogisticRegression()
X_train_pca = pca.fit_transform(X_train_std)
X_test_pca = pca.fit_transform(X_test_std)
lr.fit(X_train_pca, y_train)
plot_decision_regions(X_train_pca, y_train, classifier=lr)
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend(loc='lower left')
plt.show()
plot_decision_regions(X_test_pca, y_test, classifier=lr)
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend(loc='lower left')
plt.show()
from sklearn.svm import SVC import numpy as np import pandas as pd import matplotlib.pyplot as plt from P049 import plot_decision_regions np.random.seed(0) 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) #------------------------------------ svm = SVC(kernel="rbf", random_state=0, gamma=0.1, C=10.0) svm.fit(X_xor, y_xor) plot_decision_regions(X_xor, y_xor, classifier=svm) plt.legend(loc="upper left") plt.show()