chiral

from subprocess import PIPE
from jupyter_client import KernelManager
import time
try:
        from queue import Empty  # Py 3                                                             
except ImportError:
        from Queue import Empty  # Py 2                                                             

km = KernelManager(kernel_name='ir')
km.start_kernel()

chiral

------------------------------------------------------------
-- Quantum Shogi Program in Haskell
------------------------------------------------------------
module Main where 

import System.Environment

import Data.Maybe
import Data.List
import Data.Char

chiral

# LDA collapsed Gibbs sampler implementation in R by isobe

bows2corpus <- function(bows) {
  print("bows2corpus")

  docs <- list()
  words <- c()
  index <- list()
  last_index <- 0
  

chiral

library('rzmq')

context = init.context()
sock1 = init.socket(context,"ZMQ_PUSH")
bind.socket(sock1,"ipc:///test1")
sock2 = init.socket(context,"ZMQ_PULL")
bind.socket(sock2,"ipc:///test2")

nw <- 10000

chiral

#!/usr/bin/perl
use HTML::TreeBuilder;

our $alpha = 0.5;
our ($best,$best_rate);

our %special_alpha=('div',0.8,'span',1,'p',1,'a',0.1,
		    'ul',0.9,'ol',0.9,'li',0.9,
		    'style',0.1,'script',0.1,'noscript',0.1,
		    'b',1,'i',1,'h1',1,'h2',1,'h3',1,'font',1);

chiral

library(randomForest)
library(ROCR)

init <- function() {
  df1 <- read.csv("adult.data",header=F)
  df2 <- read.csv("adult.test",header=F)
  
  df2$V15 <- gsub("\\.$","",df2$V15)
  
  df <- rbind(df1,df2)

chiral

# -*- coding: utf-8 -*-
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize

def f(x,y):
    r1 = np.sqrt(x**2+y**2)
    r2 = np.sqrt((x-15)**2+(y-15)**2)
    return np.sin(r1)/np.sqrt(r1) + 1.5*np.exp(-r2)

chiral

import numpy as np
from random import random

def cmp0(d):
    def cmp(a,b):
        return int(a[d]-b[d])
    return cmp

def cmp1(dd):
    def cmp(a,b):

chiral

#include "ceres/ceres.h"
#include "glog/logging.h"

using ceres::CostFunction;
using ceres::AutoDiffCostFunction;
using ceres::Problem;
using ceres::Solver;
using ceres::Solve;

struct CostFunctor {

chiral

library(dplyr)
library(ggplot2)
library(reshape2)

train <- read.csv("train.csv")
#test <- read.csv("test_v2.csv")

az <- train %.%
  filter(record_type==1) %.%
  select(A:G)