dukn

import random 
import numpy as np 
from keras.models import Sequential
from keras.layers import Dense
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error

dukn

#!/bin/bash
wget --quiet https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh -O ~/anaconda.sh && \
/bin/bash ~/anaconda.sh -b -p /opt/conda && \
rm ~/anaconda.sh && \
ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
echo "conda activate base" >> ~/.bashrc

dukn

import os
import sys 
import wget
import pandas as pd 
from multiprocessing import Pool

FILE_PATH = sys.argv[1]

def get_file_paths(FILE_PATH):
    df = pd.read_csv(FILE_PATH, header=None)

dukn

import os

def count(path):
    ld = os.listdir(path)
    #print (ld)
    result = 0
    for name in ld:
        if os.path.isdir(path+'/'+name):
            #print (name)
            ld2 = os.listdir("{}/{}/".format(path,name))

dukn

from __future__ import print_function
import os
import sys
import Queue
import threading
import urllib
import time 
PARALLEL = True

if not os.path.exists("parallels"):

dukn

from __future__ import division, print_function
import numpy as np
import itertools
 
 
class FPTreeNode():
    def __init__(self, item=None, support=1):
        self.item = item
        self.support = support
        self.children = {}

dukn

Plotting

Last Update: November 4, 2016
Offline Version

Contents

• Translating
• Important Note
Basic Plot