zhenghaoz

import pandas as pd
import numpy as np


# Model
class SingleHiddenBP:

    input_number = 0
    hidden_number = 0
    output_number = 0

zhenghaoz

zhenghaoz

import matplotlib.colors as colors
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


class LogisticRegression:
    kern_param = 0
    X = np.array([])
    a = np.array([])

zhenghaoz

import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull


class KMeans:
    mean_vec = np.array([])
    X = np.array([])
    y = np.array([])
    clusters = []

zhenghaoz

import copy
import numpy as np


class Bagging:
    num_base = 0
    classifiers = []
    minvs = []
    maxvs = []

zhenghaoz

from queue import Queue
import graphviz as gv
import numpy as np
from sklearn import datasets
from sklearn.cross_validation import train_test_split


class DecisionTree:
    # Decision tree
    tree = {}

zhenghaoz

import sys, re
from pathlib import Path


def preprocessor(text):
    # Remove comments
    text = re.sub(r'\/\/.+', '', text)
    text = re.sub(r'\/\*(\*(?!\/)|[^*])*\*\/', '', text)
    # Remove the package name
    text = re.sub(r'package\s\w+(\.\w+)+;', '', text)

zhenghaoz

import requests, json, sys
from bs4 import BeautifulSoup

FILE = 'comments.txt'
PAGE = 50
TYPE = ''

def get_comments(appid):
	with open(FILE, 'a') as file:
		for page in range(1, 101):

zhenghaoz

count = 100;
dimen = 100;
test = 100;

function ret = distance(a, b)
	ret = sqrt(sum((a.-b).^2,2));
end

function ret = find_k_nearest_naive(x, p, k)
	[val, r] = sort(distance(x,p));

zhenghaoz

#include <iostream>
#include <string>
#include <cstdio>
#include <cstring>
#include <map>
#include <climits>
#include <cctype>
#include <vector>
#include <set>
#include <algorithm>