X = np.array([
[0.0, 1.5],
[1.0, 1.0],
[2.0, 2.0],
[2.0, 0.0],
[0.0, 0.0],
[1.0, 0.0],
[0.0, 1.0],
])
y = np.array([1,1,1,1,-1,-1,-1]).reshape(7,1)
w_init = np.array([4, 1, -2])
perceptron = Perceptron(X, y, plot_errors = True, plot_data_lines = True)
w_last = perceptron.train(w_init, epochs = 10)
print('Ayak weights = ', w_last)
y_pred = perceptron.predict(X)
perceptron.error(y_pred, y)
# Question 2
data = sio.loadmat('SampleCredit.mat')
X_train = data['sample'][0:500,]
X_test = data['sample'][500:,]
y_train = data['label'][0:500,0].reshape(500,1)
y_test = data['label'][500:,0].reshape(X_test.shape[0],1)
# Correct Normalized
for i in range(X_train.shape[1]):
# I am only going to use two of the features,
# Sepal width and Petal width
# for the Setos and Versicolor
iris = sklearn.datasets.load_iris()
# prepocessing data so be used with the PLA model
iris_data = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_data['Class'] = iris.target
# I will be using the first 100 data points from iris_data
iris_test = shuffle_data(iris_data, 100)
# I will input iris_test (shuffled data) and columns 0,2,4,5
x_train, y_train, x_test, y_test = train_test_data_split(iris_test, [0, 1, 3, 5])
iris_model = Perceptron()
w, sse = iris_model.train(x_train, y_train)
y_pred = iris_model.test(x_test, y_test)
print("The accuracy of the model is: ", accuracy_score(y_test, y_pred))
print("The final weights are: ", w)
print("SSE Cost")
print(sse)
# Simple scatter plot that shows the linearly seperable data.
plt.scatter(x_train[:,1], x_train[:,2], c = y_train,alpha=0.8)
plt.title("Perceptron")
plot_decision_regions(x_train[:, 1:], y_train.astype(np.integer), clf=iris_model)
plt.title('Perceptron Model')
plt.xlabel('Sepal Width [cm]')
plt.ylabel('Petal Width [cm]')
