import numpy as np
from nn import NeuralNetwork

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#   1
# 2   3
#   4
# 5   6
#   7

nn = NeuralNetwork(input_layer_size=7, hidden_layer_size=16, output_layer_size=10)
nn.load('test.npz')


test_data = [0.0, 0.0, 0.98, 0.0, 0.0, 0.99, 0.0] #1
output = nn.get(test_data)
print ("Test: 1")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')

test_data = [0.89, 0.0, 0.92, 0.97, 0.87, 0.0, 0.87] #2
output = nn.get(test_data)
print ("Test: 2")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0] #3
output = nn.get(test_data)
print ("Test: 3")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.08, 0.97, 0.98, 0.97, 0.0, 0.89, 0.12] #4
output = nn.get(test_data)
print ("Test: 4")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.82, 0.97, 0.09, 0.97, 0.0, 0.89, 0.92] #5
output = nn.get(test_data)
print ("Test: 5")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.82, 0.97, 0.09, 0.97, 0.89, 0.89, 0.92] #6
output = nn.get(test_data)
print ("Test: 6")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')

test_data = [0.82, 0.09, 0.92, 0.09, 0.08, 0.91, 0.07] #7
output = nn.get(test_data)
print ("Test: 7")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.82, 0.97, 0.91, 0.97, 0.89, 0.89, 0.92] #8
output = nn.get(test_data)
print ("Test: 8")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.82, 0.97, 0.91, 0.97, 0.08, 0.89, 0.92] #9
output = nn.get(test_data)
print ("Test: 9")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.8, 1.0, 0.98, 0.0, 0.89, 0.89, 0.99] #0
output = nn.get(test_data)
print ("Test: 0")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.1, 0.12, 0.15, 0.05, 0.09, 0.098, 0.11] # Nothing
output = nn.get(test_data)
print ("Test: Nothing")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')


test_data = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] # Nothing
output = nn.get(test_data)
print ("Test: Absolutely nothing :)")
print (output)
index = np.argmax(output) 
predict = np.argmax(output)+1
confidence = round(output[0][index][0] * 100, 2)
print (f'Predict: {predict}; Confidence: {confidence}%\r\n')