def main():
for kfold in range(10):
inputdata, outputdata = Readcsv()
trainin, trainout, testin, testout = fold(inputdata, outputdata)
data = np.array(inputdata)
out = np.array(outputdata)
wout, biout, win, biin = train(trainin, trainout)
target = test(wout, biout, win, biin, testin)
print(checkerror(target, testout))
import arff
import numpy as np
import math
from preprocessing import preprocessing, getoutputData, test, train
dataset = arff.load(open('Autism-Child-Data.arff', 'rb'))
data = np.array(dataset['data'])
#Input array
x = preprocessing(data, 21)
#Output
y = train(x)
y = getoutputData(y)
#Sigmoid Function
def sigmoid(x):
return 1 / (1 + np.exp(-x))
#Derivative of Sigmoid Function
def derivatives_sigmoid(x):
return x * (1 - x)
#Variable initialization
epoch = 5000 #Setting training iterations
lr = 0.1 #Setting learning rate
inputlayer_neurons = x.shape[1] #number of features in data set
hiddenlayer_neurons = 3 #number of hidden layers neurons
output_neurons = 1 #number of neurons at output layer
df_items = pd.read_csv('D:/TSE/python/missplaceclass/test_items.csv')
print('Building Tokenizer...')
tokenizer = preprocessing.get_tokenizer(df)
score = np.zeros(shape=(0))
label = np.zeros(shape=(0))
target_correct = 0
for i in range(len(projects)): #len(projects)
project = projects[i]
print('*' * 80)
print(project)
ss = time.time()
models = preprocessing.train(df[df.projectname != project], tokenizer,
project)
print('###########################', time.time() - ss)
#models=preprocessing.load_models(project)
ss = time.time()
tscore, tlabel, t_target_correct = preprocessing.test(
df_classes, df_items[df_items.projectname == project], tokenizer,
models)
print('###########################', time.time() - ss)
score = np.concatenate((score, tscore.reshape(-1)), axis=0)
label = np.concatenate((label, tlabel.reshape(-1)), axis=0)
target_correct += t_target_correct
print('*' * 80)
print('Final')
preprocessing.eval(score, label, target_correct)
import math
from preprocessing import preprocessing, getoutputData, test, train
np.set_printoptions(threshold='nan')
dataset = arff.load(open('Autism-Child-Data.arff', 'rb'))
data = np.array(dataset['data'])
X = preprocessing(data, 21)
def sigmoid(x, deriv=False):
if (deriv == True):
return x * (1 - x)
return 1 / (1 + np.exp(-x))
y = train(X)
# y = [[float(n) for n in m] for m in y]
o = getoutputData(X, 10)
z = test(X)
p = getoutputData(z, 282)
weight_input = 2 * np.random.random((17, 5)) - 1
weight_hidden = 2 * np.random.random((6, 1)) - 1
print o
learning_rate = [[0.01]]
print "Start : "
# code
for i in xrange(1, 1500, 1):