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))
def dealMessage(self):
Msg = self.edit.toPlainText()
self.edit.clear()
message = messageW(Msg, 'user')
item = QListWidgetItem(self.list)
message.setFixedWidth(self.width() - 25)
item.setSizeHint(message.fontRect())
self.list.setItemWidget(item, message)
self.list.scrollToBottom()
# ............................. #
# print("#$#$#$#$#$#$")
returnMsg = ""
with open("./data/9939jbks.json", "r", encoding="utf-8") as file_ks:
ks = json.load(file_ks)
ifok = True
str_msg = Msg
for ele in ks:
if ele["病名"] == str_msg:
# print(ele["挂号科室"])
for e in ele["挂号科室"]:
returnMsg = returnMsg + e + " "
ifok = False
break
# print(Msg)
# returnMsg = "jdksajhk"
if ifok:
file_tree = "./tree/decision_tree.json"
tree_test = preprocessing.Decision_Tree(ifload=True,
file_name=file_tree)
returnMsg = preprocessing.test(str_msg, tree_test)
# ............................. #
message = messageW(returnMsg, 'helper')
item = QListWidgetItem(self.list)
message.setFixedWidth(self.width() - 25)
item.setSizeHint(message.fontRect())
self.list.setItemWidget(item, message)
self.list.scrollToBottom()
import arff
import numpy as np
import math
import random
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)
o = getoutputData(x,73)
l = test(x)
u = getoutputData(x,219)
def entropy() :
kuy = math.log2(10)
return 0
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)
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):
for j in xrange(282):
input_layer = np.array([y[j]])
hidden_layer_out = sigmoid(np.dot(input_layer, weight_input))