plt.grid(1)
plt.show()
if __name__ == '__main__':
X, Y = generate_random_points(class_count=[
500,
500,
])
print("Random 2D Normally distributed Points Generated...\n")
# split the data to train test with 80:20 ratio
X_tr, X_ts, Y_tr, Y_ts = train_test_split(X, Y, test_size=0.2)
# perceptron model
model = Perceptron(shuffle=1, verbose=0)
# converting to list as the perceptron model doesn't recognize np arrays.
mat = model.fit(X_tr, Y_tr)
print("\nThe Weight and Bias :")
model.display_weight()
print("\nAccuracy : ")
print("Train Accuracy : ", model.accuracy(Y_tr, model.predict(X_tr)))
print("Test Accuracy : ", model.accuracy(Y_ts, model.predict(X_ts)))
print("No of iterations : ", model.n_iter)
if len(set(Y)) == 2:
plot_2d(X, Y, model.w, model.b)
else:
print("Not plotting as the data is not 2D")
for dot in dataset.dots:
perceptron.SetInputs([dot.x, dot.y, bias])
if (dot.y > line.f(dot.x)):
answer = 1
else:
answer = -1
guess = perceptron.Estimate()
if (guess == answer):
goodGuessCount += 1
color = ''
if (guess == 1):
color = 'ro'
else:
color = 'bo'
plt.plot(dot.x, dot.y, color)
perceptron.accuracy = goodGuessCount / len(dataset.dots)
print("Loss = " + "{:.2f}".format(1 - perceptron.GetAccuracy()))
# Drawing an estimated separation line from the model weights
w0 = perceptron.weights[0]
w1 = perceptron.weights[1]
w2 = perceptron.weights[2]
a = -(w0 / w1)
b = -(w2 / w1)
guessed_line = Function(a, b)
y2 = guessed_line.a * x + guessed_line.b
plt.plot(x, y2, 'g--') # Green dashes
# Showing the plot
plt.axis([-1, 1, -1, 1])
from perceptron import Perceptron
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
if __name__ == '__main__':
X = load_iris().data
Y = load_iris().target
# split the data to train test with 80:20 ratio
X_tr, X_ts, Y_tr, Y_ts = train_test_split(X, Y, test_size=0.2)
# perceptron model
model = Perceptron(shuffle=1, verbose=0)
# converting to list as the perceptron model doesn't recognize np arrays.
mat = model.fit(list(X_tr), list(Y_tr))
print("The Weight and Bias :")
model.display_weight()
print("\nAccuracy : ")
print("Train Accuracy : ",
model.accuracy(list(Y_tr), model.predict(list(X_tr))))
print("Test Accuracy : ",
model.accuracy(list(Y_ts), model.predict(list(X_ts))))
print("No of iterations : ", model.n_iter)
