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package src.exercises

import scala.math._
import BigInt.probablePrime
import util.Random

object chap01 {
// 1. In the Scala REPL, type 3. followed by the Tab key. What methods can be
// applied?
// => Do it in REPL. There are many methods including %, &, *, +, toByte, toChar etc.

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from azureml.core import Workspace, Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core import Image


ws = Workspace.from_config()
env = Environment(name = 'myenv')
conda_dep = CondaDependencies()
# Installs numpy version 1.17.0 conda package
conda_deps = ['blas=1.0',

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from hyperopt import hp
from hyperopt.pyll.stochastic import sample
space = hp.choice('label', [
                            hp.uniform('sub_label_1', 0.3, 0.8),
                            hp.normal('sub_label_2', 0.5, 1.0), None, 0, 1, "anything"
                           ])
space = {
        # None by default
        'max_depth': hp.choice('max_depth', [None, np.arange(1, 300, 1,dtype=int)]),
        # 2 by default

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CREATE TABLE accounts(
JE_ID serial PRIMARY KEY,
ACCOUNT_DR integer NOT NULL,
ACCOUNT_CR integer NOT NULL,
SUMM integer NOT NULL,
REVERSED integer NOT NULL,
USER_ID integer NOT NULL,
DATA integer NOT NULL,
DATE_DOC integer NOT NULL,
APPROVER_ID varchar(256) NOT NULL,

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import os
import sqlite3
import operator
from collections import OrderedDict

#path to user's history database (Chrome)
data_path = os.path.expanduser('~')+"/Library/Application Support/Google/Chrome/Default"
files = os.listdir(data_path)
history_db = os.path.join(data_path, 'history')
#querying the db

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package main

import (
	"bufio"
	"fmt"
	"io/ioutil"
	"log"
	"net/http"
	"os"
	"os/signal"

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import numpy as np
from sklearn import linear_model

def desired_marketing_expenditure(marketing_expenditure, units_sold, desired_units_sold):
    """
    :param marketing_expenditure: (list) A list of integers with the expenditure for each previous campaign.
    :param units_sold: (list) A list of integers with the number of units sold for each previous campaign.
    :param desired_units_sold: (integer) Target number of units to sell in the new campaign.
    :returns: (float) Required amount of money to be invested.
    """

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import itertools
def countPairs(arr):
    # Write your code here
    count = 0
    for i, j in itertools.combinations(arr, 2):
        v = i & j
        if v != 0 :
            if (v % 2 == 0) | (v == 1):
                count+=1
    return count

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# This is an application transferring csv data into database
# We choose a NoSQL database MongoDB since we have to store data and the logs of data GET requests# We prefer to choose PostgreSQL rather than MySQL(purely relation) or SQLite (no concurrency support)
import json
import csv
import pandas as pd
import os
from pymongo import MongoClient

cwd = os.path.dirname(__file__)
csvpath=os.path.join(cwd,'task_data.csv')

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import matplotlib.pyplot as plt
def label_prop_test(model, kernel, params_list, X_train, X_test, y_train, y_test):
    plt.figure(figsize=(20,10))
    n, g = 0, 0
    roc_scores = []
    if kernel == 'rbf':
        for g in params_list:
            lp = model(kernel=kernel, n_neighbors=n, gamma=g, max_iter=100000, tol=0.0001)
            lp.fit(X_train, y_train)
            roc_scores.append(roc_auc_score(y_test, lp.predict_proba(X_test), multi_class='ovo'))