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#!/usr/bin/env python
# -*- coding: utf-8 -*-

from gensim.models.doc2vec import Doc2Vec
from gensim.models.doc2vec import TaggedDocument
import sys


def create_model(train_data, model_name):
    with open(train_data, 'r') as f:

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

from gensim import corpora
from gensim import models
from scipy import spatial
import numpy as np
import csv
import sys

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import urllib.request
import sys
import os
import json


def merge_by_image_id(import_file):
    merged_images = []
    f = open(import_file, 'r')
    json_data = json.load(f)

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import urllib.request
import sys
import os
import json


def download(import_file, number_of_files):
    f = open(import_file, 'r')
    json_data = json.load(f)
    images = json_data["images"]

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# coding: utf-8
import numpy as np
from im2col import im2col

class Convolution:
    def __init__(self, W, b, stride=1, pad=0):
        self.W = W
        self.b = b
        self.stride = stride
        self.pad = pad

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import numpy as np
from node import _Node
from sklearn.base import ClassifierMixin


class DecisionTree(ClassifierMixin):
    def __init__(self):
        self._root = None

    def create_tree(self, X, y):

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if __name__ == '__main__':
    from sklearn import datasets
    from sklearn.model_selection import train_test_split
    from random_forest import RandomForest

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    X_train, X_test, y_train, y_test = train_test_split(