from sklearn.ensemble import RandomForestClassifier
import numpy as np
import matplotlib.pyplot as plt
from Util import plot_decision_regions
from IrisData import getIrisData
X_train, X_test, X_combined, y_train, y_test, y_combined = getIrisData(
standardized=False)
forest = RandomForestClassifier(criterion='entropy',
n_estimators=10,
random_state=1,
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 [cm]')
plt.ylabel('petal width [cm]')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from Util import plot_decision_regions
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
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)
gamma = 1
svm = SVC(kernel='rbf', C=10.0, random_state=0, gamma=gamma)
svm.fit(X_xor, y_xor)
plot_decision_regions(X_xor, y_xor, classifier=svm)
plt.legend(loc='upper left')
plt.title("RBF Kernel SVM ($\gamma $ = %f)" % gamma)
plt.show()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=1,
stratify=y)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
ppn = Perceptron(n_iter=40, eta0=0.1, random_state=1)
ppn.fit(X_train_std, y_train)
y_pred = ppn.predict(X_test_std)
# print('Misclassified samples: %d' % (y_test != y_pred).sum())
# print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined_std = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std,
y=y_combined_std,
classifier=ppn,
test_idx=range(105, 150))
plt.xlabel('ptal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
from Wine import getWineData
from Util import plot_decision_regions
from sklearn.lda import LDA
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt
X_train_std, X_test_std, y_train, y_test = getWineData()
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('LD 1')
plt.ylabel('LD 2')
plt.legend(loc='lower left')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from Util import plot_decision_regions
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
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)
lr = LogisticRegression(C=1000.0, random_state=0)
lr.fit(X_train_std, y_train)
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X_combined_std, y_combined, lr, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
