k5trismegistus

/*
 * Your task is to implement the sum() function so that it computes the sum of
 * all elements in the given array a.
 */

fun sum(a: IntArray): Int {
    when {
        a.size > 0 -> return a.reduce{s1, s2 -> s1 + s2}
        else -> println("Array must be longer than 2")
    }

k5trismegistus

import matplotlib.pyplot as plt
import numpy
import random

def create_datas():
    datas = []
    for i in range(20):
        a = 10 * (random.random() -  0.5)
        datas.append((a, a + 3 * (random.random() - 0.5)))
    return datas

k5trismegistus

import matplotlib.pyplot as plt
import numpy

def pca(datas, dim):
    cov_matrix = cov(transposition(datas))
    
    evs = eigen_vectors(cov_matrix)
    
    return [tuple(sum([a*b for a, b in zip(v,d)]) for v in evs[:dim]) for d in datas]
    

k5trismegistus

import numpy
import random

def create_datas(dimension):
    datas = []
    for i in range(50):
        explanatory = [random.random() for i in range(dimension - 1)]
        criterion = sum([d * (i + 1) for i, d in enumerate(explanatory)]) + 4
        datas.append( (explanatory, criterion + random.random() * 0.3) )
    return datas

k5trismegistus

import random

def create_datas():
    datas = []
    for i in range(50):
        a = random.random()
        datas.append((a, 3 * a + 2 + (random.random() - 0.5)))
    return datas

def regression_analysis(datas):

k5trismegistus

def regression_analysis(datas):
    # x, yの平均値を求める
    x_ave = sum([d[0] for d in datas]) / len(datas)
    y_ave = sum([d[1] for d in datas]) / len(datas)
    
    # x, yの分散、xyの共分散を求める
    c_xy = sum([(d[0] - x_ave) * (d[1] - y_ave) for d in datas]) / len(datas)
    d_x = sum([(d[0] - x_ave) ** 2 for d in datas]) / len(datas)
    d_y = sum([(d[1] - y_ave) ** 2 for d in datas]) / len(datas)
    

k5trismegistus

import random

def create_datas():
    datas = []
    for i in range(50):
        a = random.random()
        datas.append((a, 3 * a + 2 + (random.random() - 0.5)))
    return datas

k5trismegistus

import random
import itertools
import neuron

from IPython import embed
from IPython.terminal.embed import InteractiveShellEmbed

class NeuralNetwork():
    
    def __init__(self, layers, learn_rate):

k5trismegistus

test = [1, 2, 3, [4, 5], 6, 7, [8, [9, 10]], [[11, 12, [13, 14]]]]

def greedy_flatten(seq):
    if not isinstance(seq, list):
        return seq
    
    seq = seq.copy()
    while any([isinstance(x, list) for x in seq]):
        for idx, x in enumerate(seq):
            if isinstance(seq[idx], list):

k5trismegistus

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pygame
from pygame.locals import *
import serial
import sys
import re
import time