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data_plotter.py
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data_plotter.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = np.load('data.npy')
neural_data = np.load('NN_data.npy')
labels = np.load('labels.npy')
data_featurenames = ("play0", "play1", "play2", "play3", "play4", "round", "score")
neural_featurenames = ("meR0", "meP0", "meS0", "enemyR0", "enemyP0", "enemyS0", "meR1", "meP1", "meS1", "enemyR1", "enemyP1", "enemyS1",
"meR2", "meP2", "meS2", "enemyR2", "enemyP2", "enemyS2", "meR3", "meP3", "meS3", "enemyR3", "enemyP3", "enemyS3",
"meR4", "meP4", "meS4", "enemyR4", "enemyP4", "enemyS4", "round", "score")
#for idx in range(len(data_featurenames)):
# for idx2 in range(idx+1, len(data_featurenames)):
# plt.figure()
# plt.xlabel(data_featurenames[idx])
# plt.ylabel(data_featurenames[idx2])
# rocks_x = []
# papers_x = []
# scissors_x = []
# rocks_y = []
# papers_y = []
# scissors_y = []
# for label_idx in range(len(labels)):
# if labels[label_idx] == 0:
# rocks_x.append(data[label_idx][idx])
# rocks_y.append(data[label_idx][idx2])
# if labels[label_idx] == 1:
# scissors_x.append(data[label_idx][idx]+0.1)
# scissors_y.append(data[label_idx][idx2]+0.1)
# if labels[label_idx] == 2:
# papers_x.append(data[label_idx][idx]+0.2)
# papers_y.append(data[label_idx][idx2]+0.2)
# plt.scatter(rocks_x,rocks_y, color='gray')
# plt.scatter(papers_x,papers_y, color='green')
# plt.scatter(scissors_x,scissors_y, color='red')
# plt.show()
for idx in range(len(neural_featurenames)):
rocks = np.zeros(10)
papers = np.zeros(10)
scissors = np.zeros(10)
for label_idx in range(len(labels)):
if idx < 30:
value = int(neural_data[label_idx][idx])
elif idx == 30:
value = int(neural_data[label_idx][idx]*10-1)
elif idx == 31:
value = int(neural_data[label_idx][idx]*2+2)
if labels[label_idx] == 0:
rocks[value] += 1
if labels[label_idx] == 1:
papers[value] += 1
if labels[label_idx] == 2:
scissors[value] += 1
for i in range(10):
sum = rocks[i]+papers[i]+scissors[i]
rocks[i] = rocks[i]/sum
papers[i] = papers[i]/sum
scissors[i] = scissors[i]/sum
plt.figure()
plt.xlabel(neural_featurenames[idx])
plt.ylabel('Amount')
plt.plot(rocks, label='Rock')
plt.plot(papers, label = 'Paper')
plt.plot(scissors, label = 'Scissor')
plt.legend()
plt.show()