def PlotModel(learning_rate, X, y, iterations_count):
model = Adaline(iterations_count=iterations_count,
learning_rate=learning_rate)
model.fit(X, y)
plot_decision_regions(X, y, model)
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.title("Adaline - Learning rate %s" % learning_rate)
plt.show()
plt.plot(range(1, len(model.cost_) + 1), model.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Error')
plt.show()
print('Class labels:', np.unique(y))
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=1)
print('Labels counts in y:', np.bincount(y))
print('Labels counts in y_train:', np.bincount(y_train))
print('Labels counts in y_test:', np.bincount(y_test))
standardScaler = StandardScaler()
standardScaler.fit(X_train)
X_train_std = standardScaler.transform(X_train)
X_test_std = standardScaler.transform(X_test)
model = SVC(kernel='rbf', C=5., random_state=1, gamma=1)
model.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=X_combined_std,
y=y_combined,
classifier=model,
test_idx=range(105, 150))
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.show()
import matplotlib.pyplot as plt
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
print('Class labels:', np.unique(y))
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=1)
print('Labels counts in y:', np.bincount(y))
print('Labels counts in y_train:', np.bincount(y_train))
print('Labels counts in y_test:', np.bincount(y_test))
standardScaler = StandardScaler()
standardScaler.fit(X_train)
X_train_std = standardScaler.transform(X_train)
X_test_std = standardScaler.transform(X_test)
X_train_01_subset = X_train[(y_train == 0) | (y_train == 1)]
y_train_01_subset = y_train[(y_train == 0) | (y_train == 1)]
model = LogisticRegresion(iterations_count=1000, learning_rate=0.05, seed=1)
model.fit(X_train_01_subset, y_train_01_subset)
plot_decision_regions(X=X_train_01_subset,
y=y_train_01_subset,
classifier=model)
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header=None)
print(df.tail())
y = df.iloc[:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[:100, [0, 2]].values
plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa')
plt.scatter(X[50:, 0], X[50:, 1], color='blue', marker='x', label='versicolor')
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()
model = Perceptron(iterations_count=10)
model.fit(X, y)
plt.plot(range(1, len(model.errors_) + 1), model.errors_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Number of updates')
plt.show()
plot_decision_regions(X, y, classifier=model)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()
weights, params = [], []
for c in np.arange(-5, 5):
model = LogisticRegression(C=10.**c, random_state=1)
model.fit(X_train_std, y_train)
weights.append(model.coef_[1])
params.append(100.**c)
weights = np.array(weights)
plt.plot(params, weights[:, 0], label='petal length')
plt.plot(params, weights[:, 1], linestyle='--', label='petal width')
plt.xlabel('weight coefficient')
plt.ylabel('C')
plt.legend(loc='upper left')
plt.xscale('log')
plt.show()
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,
classifier=model,
test_idx=range(int(X.shape[0] * 0.7), X.shape[0]))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
model = LogisticRegression(penalty='l1', C=1.)
wine_model_testing(model)