penut85420

#include <stdio.h>

int main() {
	int h;
	int i, j, k;
	
	printf("Height of triangle: ");
	scanf("%d", &h);

	for (i = 1; i <= h; i++) {

penut85420

const int interruptNumber = 0;
const int buttonPin = 2;
const int ledPin = 8; // 天使眼LED
int state;
int arr[] = {3, 5, 6, 9, 10, 11};

void setup() {
  pinMode(ledPin, OUTPUT);
  pinMode(buttonPin, INPUT);
  pinMode(interruptNumber, INPUT_PULLUP);

penut85420

package ntou.ws.ui;

import javafx.application.Application;
import javafx.geometry.*;
import javafx.scene.Scene;
import javafx.scene.control.*;
import javafx.scene.layout.*;
import javafx.stage.Stage;

public class SegmentorApp extends Application {

penut85420

#include <iostream>
#include <cstdlib>
#include <fstream>
#include <iomanip>
using namespace std;

char id2ch(int);

typedef struct process {
    int pid;

penut85420

import numpy as np
from numpy import concatenate as c

x = np.array(range(100))
y = np.array(range(100, 200))

def kfold_split(x, y, sp):
    seg_len = int(len(x) / sp)
    for i in range(seg_len):
        seg_begin = i * seg_len

penut85420

#include <stdio.h>

int main() {
    int arr[10] = {9, 5, 7, 3, 1, 2, 8, 6, 4, 0};
    int i, j, t, m; //, p; // 如何不使用 p 這個變數完成選擇演算法?

    for (i = 0; i < 10; i++) {
        m = arr[0]; // 紀錄陣列中最大值為多少
        p = 0;      // 記錄此最大值位於陣列的第幾個元素
        for (j = 1; j < 10 - i; j++) {

penut85420

@start javaw -Dfile.encoding=UTF-8 -jar CORPRO.jar

penut85420

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",

penut85420

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<pageset>
    <page id="386">
        <title>上月城</title>
        <text>上月城 是 位 在 兵庫縣 佐用郡 佐用町 的 一座 山城 。 </text>
        <category>
            <cat>兵庫縣城堡</cat>
            <cat>室町時代建築</cat>
            <cat>赤松氏</cat>
            <cat>尼子氏</cat>

penut85420

Loader = importlib.import_module('Loader')
model_path = "w2v\\w2v_word_model.npy"
dict_path = "w2v\\w2v_word_dictionary.pkl"
w2v_model = Loader.Word2VecLoader(model_path, dict_path)
w2v, d = w2v_model.load_w2v()

w2v_list = list()
with open("a.txt", "r", encoding='utf-8') as fin:
    for line in fin:
        v = []