import numpy as np
from perceptron import Perceptron
from display import Display
PERCEPTRON = Perceptron(input_size=2, lr=0.01)
x_y = np.array((((0, 0), 1), ((1, 1), 0)))
for i in range(1000):
for (x, y) in x_y:
output = PERCEPTRON.forward(x)
error = output - y
PERCEPTRON.tune_parameters(x, error)
DISPLAY = Display()
DISPLAY.display(perceptron=PERCEPTRON)
testY = test['species'].astype('category')
testY = testY.cat.codes
testY = testY / testY.max()
trainX_flatten = trainX.reshape(trainX.shape[0], -1).T
testX_flatten = testX.reshape(testX.shape[0], -1).T
trainX_flatten = (trainX_flatten/trainX_flatten.max())
testX_flatten = (testX_flatten/testX_flatten.max())
#W, b, cost = p.fit(trainX_flatten, trainY, True)
x = np.arange(1, 51)
"""
np.savetxt("./save/weights.txt", W)
np.savetxt("./save/cost.txt", cost)
saveBias(b, "./save/bias.txt")
"""
W = np.loadtxt("./save/weights.txt")
cost = np.loadtxt("./save/cost.txt")
b = loadBias("./save/bias.txt")
predictions = p.forward(testX_flatten, W, b)
print("accuracy: ", accuracy(testY, predictions))
plt.plot(x, cost)
plt.show()