itarato

found = False


def solve(results, parts, sums, idx):
    global found
    if found:
        return True

    if sums == results:
        found = True

itarato

# Doxyfile 1.8.9

#---------------------------------------------------------------------------
# Project related configuration options
#---------------------------------------------------------------------------
DOXYFILE_ENCODING      = UTF-8
PROJECT_NAME           = "Cameron & Wilding Tool"
PROJECT_NUMBER         = 
PROJECT_BRIEF          = "Drupal 7 Architecture tool"
PROJECT_LOGO           = /Users/itarato/Desktop/logo.png

itarato

import System.IO
import Control.Monad

solve_v1 :: [Int] -> Int
solve_v1 (a:[]) = 0
solve_v1 (a:b:c) = (if a > b then a - b else 0) + (solve_v1 (b:c))

min_dist :: [Int] -> Int
min_dist (a:[]) = 0
min_dist (a:b:c) = if a > b then max (min_dist (b:c)) (a - b) else min_dist (b:c)

itarato

import numpy as np
import matplotlib.pyplot as plt


def plot_result(O):
    sample_size = 50
    linX = np.linspace(np.min(npdata[:,1]), np.max(npdata[:,1]), num=sample_size)
    testX = np.concatenate((np.ones((sample_size, 1)), linX.reshape((sample_size, 1))), axis=1)
    y = np.dot(testX, O)
    plt.plot(linX, y[:,0], 'r-')

itarato

import numpy as np
import matplotlib.pyplot as plt


def plot_result(O):
    sample_size = 50
    linX = np.linspace(np.min(npdata[:,1]), np.max(npdata[:,1]), num=sample_size)
    testX = np.concatenate((np.ones((sample_size, 1)), linX.reshape((sample_size, 1))), axis=1)
    y = np.dot(testX, O)
    plt.plot(linX, y[:,0], 'r-')

itarato

import numpy as np
import matplotlib.pyplot as plt


if __name__ == '__main__':
    fin = file('logreg.txt', 'r')
    npdata = np.genfromtxt(fin, delimiter=',')
    posData = npdata[npdata[:,2] == 1,:]
    negData = npdata[npdata[:,2] == 0,:]

itarato

import numpy as np


class NeuralNetwork:
    def __init__(self, data, layer_sizes):
        """
        :param data: (M)x(N+1) matrix of inout data, Y is in last column.
        :param iteration: Iteration count for training.
        """
        self.data = data

itarato

package main

import (
	"bytes"
	"io/ioutil"
	"log"
)

func temp(...interface{}) {}

itarato

package main

import (
	"log"
)

var (
	n    int          = 8
	l    []int        = make([]int, n*2)
	used map[int]bool = make(map[int]bool)

itarato

import turtle


limit = 6
frac = 0.8


def draw(size, inv = False):
    if size > limit:
        t.lt(90)