def q3():
# The shuffling is for test examples of skin and non skin
# (without shuffling we test only non skins)
examples = np.random.permutation(get_mat('Skin_NonSkin.txt'))
powers = 2**np.arange(-5, 16, 2.0)
learning_part = 0.7
learning, testing = np.split(examples, [examples.shape[0]*learning_part])
C1, errors = find_c(powers, learning)
error = sum([r[-1] * C1(r[:-1]) < 0 for r in testing])
error /= testing.shape[0]
return error, np.array([powers,errors])
def skin_mat():
examples = np.random.permutation(get_mat('Skin_NonSkin.txt'))
powers = 2**np.arange(-5, 16, 2.0)
classifier, errors = find_c(powers, examples)
img = misc.imread('image_0009.jpg')
skin = np.array(img[..., 0])
m, n = skin.shape
for i in range(m):
for j in range(n):
r, g, b = img[i, j]
s = classifier(np.array([b, g, r]))
skin[i, j] = 255 if s > 0 else 0
return skin
#
# Copyright © 2018 weihao <*****@*****.**>
#
# Distributed under terms of the MIT license.
from get_mat import get_mat
from get_pqrst import get_pqrst
from get_features import get_features
from get_ground_truth import get_result_for_classifier
from utils import *
from train import train
import pandas as pd
if __name__ == '__main__':
data_used = 1000
data = []
#for i in range(1, 8529):
for i in range(1, data_used):
print(i)
file_name = '../training2017/A%s.mat' % str(i).zfill(5)
out = get_mat(file_name)
ecg = out['filtered']
P_index, Q_index, R_index, S_index, T_index = get_pqrst(out)
features = get_features(ecg, P_index, Q_index, R_index, S_index,
T_index)
data.append(flatten(features))
df = pd.DataFrame(data=data)
target = get_result_for_classifier('../training2017/REFERENCE.csv', 1)
train(df, target[:data_used - 1])
