def generate_data(self):
if self.train:
left_data, right_data, label = get_data(True)
return left_data, right_data, label
else:
left_data, right_data = get_data(False)
return left_data, right_data
def create_test():
test_data = request.get_json()
global test_
test_ = {
"subject": test_data["subject"],
"answer_keys": test_data['answer_keys']
}
t.create_table()
for i in test_.keys():
entities.append(test_[i])
t.insert_into_tests(entities)
ret_data = t.get_data()
ret_val = {
"test_id": ret_data[0][0],
"subject": ret_data[0][1],
"answer_keys": eval(ret_data[0][2]),
"submissions": ret_data[0][3]
}
return ret_val, 201
import os
from test import output_scale_recover, get_data
os.environ[
'TF_CPP_MIN_LOG_LEVEL'] = '2' ##for avx command set in CPU :https://blog.csdn.net/hq86937375/article/details/79696023
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import optimizers
from keras.layers.normalization import BatchNormalization #ref:https://www.zhihu.com/question/55621104
from keras import initializers
from keras import callbacks
from keras.models import model_from_json
np.random.seed(10)
inputLen = 20
data = get_data()
#interface variable
name = ""
from_data = 0
to_data = 1
#private:
#user interface
def main():
if len(sys.argv) < 7: #
print(
"Usage:", sys.argv[0],
"--name <test name> --from <the index test_data from> --to <the index test_data to>"
from test import get_data print(get_data())
def params():
return str_data(get_data('StringReplace'))
def params():
return str_data(get_data('StringFind'))
def params():
return str_data(get_data('StringConcat'))
def params():
return str_data(get_data('StringEquals'))
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy
import pylab
import test as t
# intial parameters
z = t.get_data()
n_iter = len(z)
sz = (n_iter,) # size of array
Q = 1e-2 # process variance
# allocate space for arrays
xhat=numpy.zeros(sz) # a posteri estimate of x
P=numpy.zeros(sz) # a posteri error estimate
xhatminus=numpy.zeros(sz) # a priori estimate of x
Pminus=numpy.zeros(sz) # a priori error estimate
K=numpy.zeros(sz) # gain or blending factor
R = 0.2**2 # estimate of measurement variance, change to see effect
# intial guesses
xhat[0] = z[0]
P[0] = 50.0
for k in range(1,n_iter):
# time update
xhatminus[k] = xhat[k-1]
Pminus[k] = P[k-1]+Q
def params():
return int_data(get_data('GCD'))
def params():
return int_data(get_data('Loop'))
def params():
return int_data(get_data('FactorialBig'))
def post_tasks():
name = request.json['name']
return jsonify(get_data(name))
def params():
return long_data(get_data('MultiplicationOfBigInt'))
def params():
return long_data(get_data('MultiplicationOfLong'))
def params():
return int_data(get_data('MultiplicationOfInt'))