codekansas

from __future__ import print_function

from time import sleep
import os

type_to_rep = {
    'empty': '.',
    'head': 'H',
    'tail': 'T',
    'conductor': '=',

codekansas

from __future__ import print_function

import theano
import theano.tensor as T
import numpy as np
import time

X = theano.shared(value=np.asarray([[0, 1], [1, 0], [0, 0], [1, 1]]), name='X')
y = theano.shared(value=np.asarray([[0], [0], [1], [1]]), name='y')
rng = np.random.RandomState(1234)

codekansas

// reference: http://codeforces.com/contest/697/problem/C

import java.util.HashMap;
import java.util.Map;
import java.util.Scanner;

public class Codeforces697ProblemC {
	public static void main(String... args) {
		Scanner s = new Scanner(System.in);
		

codekansas

[>....................]how compare home insurance
Predicted: ([ 0.39381921])in order to calculate your annual premium for home insurance it be necessary answer some basic question about the risk and obtain a quote basic characteristic of the home will include the year build , square footage , type of roof , foundation type , number of bathroom , style of home , prior claim and identify information of the owner you may obtain a quote and buy home insurance online use an online link in multiple state ; it may be use by escrow / closing professional , mortgage professional , real estate agent directly consumer an exemplary online experience for all WEBSITELINK
Expected: ([ 0.38000157])start by talk to your Insurance Professional it be important not only compare coverage offering and policy extension and limitation , but also the company that be offer this protection proposal and suggest coverage shall be base upon the result of a careful Reconstruction Analysis which will determine the amount of recommend covera

codekansas

from __future__ import print_function

import numpy as np
from keras.engine import Input, Model
from keras.layers import Dense

X = np.asarray([[0, 1], [1, 0], [0, 0], [1, 1]])
y = np.asarray([[0], [0], [1], [1]])

input = Input(shape=(2,))

codekansas

#!/usr/bin/env python
"""Example of building a model to solve an XOR problem in Keras."""

import keras
import numpy as np

# XOR data.
x = np.array([
        [0, 1],
        [1, 0],

codekansas

import theano
import theano.tensor as T
import numpy as np

X = theano.shared(value=np.asarray([[1, 0], [0, 0], [0, 1], [1, 1]]), name='X')
y = theano.shared(value=np.asarray([[1], [0], [1], [0]]), name='y')
rng = np.random.RandomState(1234)
LEARNING_RATE = 0.01

codekansas

; Finds all factors of a number in O(sqrt n) time.
(define (factors n)
  (define (@factors n i a)
    (cond ((= (modulo n i) 0) (@factors (quotient n i) i (cons i a)))
          ((>= (* i i) n) (if (= 1 n) a (cons n a)))
          (else (@factors n (+ i 1) a))))
  (@factors n 2 `()))

; Multiples all the elements in a list.
(define (mult l)

codekansas

from keras.optimizers import SGD, Adagrad, RMSprop, Adadelta, Adam, Adamax
import keras.backend as K

DEFAULT_NOISE = 0.05


def ballpark_gradient(gradient, noise):
    return [g * K.random_normal(shape=K.shape(g), mean=1.0, std=noise) for g in gradient]

codekansas

#!/usr/bin/env python
"""The training script for the DANN model."""

from __future__ import division
from __future__ import print_function

import csv
import os
import itertools
import sys