xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
# plot class samples
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
c=cmap(idx),
marker=markers[idx],
label=cl)
ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)
ada.fit(X_std, y)
plot_decision_regions(X_std, y, classifier=ada)
plt.title('Adaline - Stochastic Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.show()
plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Average Cost')
plt.show()
import numpy as np
from adalineSGD import AdalineSGD
from mainPerceptron import plot_decision_regions
# Print the tail of the data
df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
# Visualisation the Data
y = df.iloc[0:100, 4].values
print(y)
y = np.where(y == 'Iris-setosa', -1, 1)
print(y)
X = df.iloc[0:100, [0, 2]].values
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)
ada.fit(X_std, y)
plot_decision_regions(X_std, y, classifier=ada)
plt.title('Adaline - Stochastic Gradient descent')
plt.xlabel('sepal length [standardized')
plt.ylabel('petal length [standardized')
plt.legend(loc='upper left')
plt.show()
plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Average Cost')
plt.show()
perceptron algorithm and look at the number of errors make during each
epoch.
"""
from adalineGD import AdalineGD
from adalineSGD import AdalineSGD
# Standardize data
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
adln = AdalineGD(eta=0.01, n_iter=25)
adln.fit(X_std, y)
adln1 = AdalineSGD(eta=0.01, n_iter=25)
adln1.fit(X_std, y)
plt.plot(range(1,
len(adln1.errors_) + 1),
adln1.errors_,
marker='x',
color='red',
label='AdalineSGD')
plt.plot(range(1,
len(adln.errors_) + 1),
adln.errors_,
marker='o',
color='blue',
label='AdalineGD')
plt.xlabel('Epoch #')
plt.ylabel('Errors')
from init_obj import create_universe, prepare_data, show_universe, test_model from adaline import Adaline from adalineSGD import AdalineSGD import numpy as np groups = create_universe() X, Y = prepare_data(groups) # show_universe(groups) y = np.where(Y == 'A', 1, -1) model = Adaline(0.0001, 50) model.fit(X, y) # stochastic model2 = AdalineSGD(0.0001, 50) model2.fit(X, y) test_model(X, model, model2)
marker=markers[idx],
label=cl)
# read the data
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header=None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
x = df.iloc[0:100, [0, 2]].values
# standardizing instead
x_std = np.copy(x)
x_std[:, 0] = (x[:, 0] - x[:, 0].mean()) / x[:, 0].std()
x_std[:, 1] = (x[:, 1] - x[:, 1].mean()) / x[:, 1].std()
ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)
ada.fit(x_std, y)
plot_decision_regions(x_std, y, classifier=ada)
plt.title('Adaline - Stochastic Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.show()
plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Average Cost')
plt.show()
plt.legend(loc='upper left DG')
plt.tight_layout()
# plt.savefig('./adaline_2.png', dpi=300)
plt.show()
plt.plot(range(1,len(ada.cost_)+1),ada.cost_, marker='o')
plt.xlabel('Epochs DG')
plt.ylabel('sum-squared-error DG')
plt.tight_layout()
plt.show()
print("=======================ADALINE=========================")
print("=======================SDG=========================")
adaSDG = AdalineSGD(n_iter=15, eta=0.01, random_state=1)
adaSDG.fit(X_std, y)
fc.plot_dexision_regions(X_std, y, classifier=adaSDG)
plt.title('AdalineSDG - Stochastic Gradient Descent')
plt.xlabel('sepal length [standardized]SDG')
plt.ylabel('petal length [standardized]SDG')
plt.legend(loc='upper left SDG')
plt.tight_layout()
#plt.savefig('./adaline_4.png', dpi=300)
plt.show()
plt.plot(range(1, len(adaSDG.cost_) + 1), adaSDG.cost_, marker='o')
plt.xlabel('Epochs SDG')
plt.ylabel('Average Cost SDG')
plt.tight_layout()