CS

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import random
from scipy import stats


def get_t_value():
    treatmenDist = (119.5, 5.0)
    controlDist = (120, 4.0)
    sampleSize = 1000

    treatmentTimes, controlTimes = [], []

    for s in range(sampleSize):
        treatmentTimes.append(random.gauss(treatmenDist[0], treatmenDist[1]))
        controlTimes.append(random.gauss(controlDist[0], controlDist[1]))

    controleMeans = sum(controlTimes) / len(controlTimes)
    treatMeans = sum(treatmentTimes) / len(treatmentTimes)

    twoSampleTest = stats.ttest_ind(treatmentTimes, controlTimes, equal_var=False)
    print('Treatmeant mean - control mean = ',
          treatMeans - controleMeans, ' minutes')
    print('The t-statistic from two-sample test is ', twoSampleTest[0])
    print('The p-value from two-sample test is ', twoSampleTest[1])


for i in range(10):
    twoSampleTest = get_t_value()

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import random
import pylab
import csv

def variance(X):
    """分散"""
    mean = sum(X)/len(X)
    tot = 0.0
    for x in X:
        tot += (x - mean)**2
    return tot/len(X)


def stdDev(X):
    """標準偏差(分散の平方根)"""
    return variance(X)**0.5

def read_anime_score(path):
    """データを読み込みスコアのリストを返す"""
    scores = []
    with open(path, newline='') as f:
        reader = csv.reader(f)
        # skip header
        next(reader)
        for row in reader:
            scores.append(float(row[15]))

    return scores

data = read_anime_score('./anime_cleaned.csv')

# 母平均
popMean = sum(data) / len(data)
# 標本サイズ
sampleSize = 200
numBad = 0
for t in range(10000):
    sample = random.sample(data, sampleSize)
    # 標本平均
    sampleMean = sum(sample) / sampleSize
    # 標準誤差
    se = stdDev(sample) / sampleSize**0.5

    # 母平均と標本平均の誤差から信頼区間外のデータ数を求める
    if abs(popMean - sampleMean) > 1.96 * se:
        numBad += 1
print('Fraction outside 95% confidence interval =', numBad / 10000)

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import pylab
import random


def janken():
    JANKEN_PATTERN = ['G', 'C', 'P']
    while True:
        a_t = random.choice(JANKEN_PATTERN)
        b_t = random.choice(JANKEN_PATTERN)
        if a_t != b_t:
            break
    return janken_judge(a_t, b_t)


def janken_kai():
    JANKEN_WIN_PATTERN = ['G', 'G', 'C', 'P']
    JANKEN_LOSE_PATTERN = ['G', 'C', 'C', 'P']
    while True:
        a_t = random.choice(JANKEN_WIN_PATTERN)
        b_t = random.choice(JANKEN_LOSE_PATTERN)
        if a_t != b_t:
            break
    return janken_judge(a_t, b_t)


def janken_judge(a_t, b_t):
    """ a_tとb_tは違う値の想定。
    aが勝っていたらTrue, 負けていたらFalseを返す """

    win_pattern = [('G', 'C'), ('C', 'P'), ('P', 'G')]
    return (a_t, b_t) in win_pattern


def show_plot(trials, target):
    a_rate = []
    a_win = 0
    for k in range(1, trials + 1):
        if target():
            a_win += 1

        a_rate.append(a_win / k * 100)
    pylab.plot(a_rate)
    pylab.ylim(0, 100)
    pylab.xlim(1, trials)
    pylab.xlabel('times')
    pylab.ylabel('rate')
    pylab.title('Junken')
    pylab.show()


def get_stdDev(trials):
    a_rate = []
    for i in range(0, 100):
        a_win = 0
        for k in range(0, trials):
            if janken():
                a_win += 1
        a_rate.append(a_win / k * 100)
    return stdDev(a_rate)


def variance(X):
    """分散"""
    mean = sum(X)/len(X)
    tot = 0.0
    for x in X:
        tot += (x - mean)**2
    return tot/len(X)


def stdDev(X):
    """標準偏差(分散の平方根)"""
    return variance(X)**0.5


show_plot(1000, janken)
show_plot(1000, janken_kai)
print('10 trials stdDev: ', get_stdDev(10))
print('100 trials stdDev: ', get_stdDev(100))
print('1000 trials stdDev: ', get_stdDev(1000))

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import math
import pylab

def binomical_distribution(k, p, t):
    c = math.factorial(k) / (math.factorial(t) * math.factorial(k - t))
    return c * p**t * (1 - p)**(k - t)


def show_plot(p, t):
    x, y = [], []
    for k in range(t, 101):
	x.append(k)
	y.append(binomical_distribution(k, p, t))

    pylab.plot(x)
    pylab.plot(y)
    pylab.ylim(0, 0.5)
    pylab.xlim(2, 100)
    pylab.xlabel('k')
    pylab.ylabel('p')
    pylab.title('Binomical Distribution')
    pylab.show()

show_plot(1 / 6, 2)

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class Node():

    def __init__(self, node, freq):
        self.freq = freq
        self.node_list = []
        self.node_list.append(node)

    def extend(self, node):
        self.node_list.extend(node.node_list)

    def __lt__(self, other):
        return self.freq < other.freq

def huffman(nodes):
    from heapq import heappush, heappop
    huffmanQueue = []
    ans = {}

    # 優先度つきキューにノードを格納する
    for node in nodes:
        heappush(huffmanQueue, (node.freq, node))

    while len(huffmanQueue) > 1:
        (leftFreq, leftNode) = heappop(huffmanQueue)
        (rightFreq, rightNode) = heappop(huffmanQueue)
        # 一番頻度が少ないノードに'0'を追加
        for s in leftNode.node_list:
            ans[s] = '0' + ans.get(s, '')

        # 二番目に頻度が少ないノードに'1'を追加
        for s in rightNode.node_list:
    	    ans[s] = '1' + ans.get(s, '')

        totalFreq = leftFreq + rightFreq
        leftNode.extend(rightNode)
        heappush(huffmanQueue, (totalFreq, leftNode))
    return ans

print(huffman([Node('a', 5), Node('b', 35), Node('c', 25), Node('d', 15), Node('e', 30)]))

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class Node(object):
    def __init__(self, name):
        self.name = name

    def getName(self):
        return self.name

    def __str__(self):
        return self.name

    def __lt__(self, i):
        return None


class WeightedEdge(object):
    """重み付きの枝を表すクラス"""

    def __init__(self, src, dest, weight):
        self.src = src
        self.dest = dest
        self.weight = weight

    def getSource(self):
        return self.src

    def getDestination(self):
        return self.dest

    def getWeight(self):
        return self.weight

    def __str__(self):
        return self.src.getName() + '->(' + str(self.weight) + ')' + self.dest.getName()


class Digraph(object):
    """有向グラフを表すクラス"""

    def __init__(self):
        self.nodes = []
        self.edges = {}

    def addNode(self, node):
        if node in self.nodes:
            raise ValueError('Duplicate node')
        else:
            self.nodes.append(node)
            self.edges[node] = []

    def addEdge(self, edge):
        src = edge.getSource()
        dest = edge.getDestination()
        weight = edge.getWeight()
        if not (src in self.nodes and dest in self.nodes):
            raise ValueError('Node not a graph')

        self.edges[src].append((weight, dest))

    def childrenOf(self, node):
        return self.edges[node]

    def __str__(self):
        result = ''
        for src in self.nodes:
            for dest in self.edges[src]:
                result = result + src.getName() + '->' + dest.getName() + '\n'
        return result[:-1]


def printPath(path):
    """パスを表示するメソッド"""
    result = ''
    for i in range(len(path)):
        result = result + str(path[i])
        if i != len(path) - 1:
            result = result + '->'
    return result


def UCS(graph, start, end):
    """重み付き有向グラフを探索するメソッド"""

    initPath = [start]
    pathQueue = []

    from heapq import heappush, heappop
    heappush(pathQueue, (0, initPath))

    while len(pathQueue) != 0:
        (weight, tmpPath) = heappop(pathQueue)

        print('Current UCS path:', printPath(tmpPath), ' weight:', weight)
        lastNode = tmpPath[-1]

        if lastNode == end:
            return tmpPath
        for (next_weight, nextNode) in graph.childrenOf(lastNode):
            if nextNode not in tmpPath:
                newPath = tmpPath + [nextNode]
                heappush(pathQueue, (weight + next_weight, newPath))


def BFS(graph, start, end):
    """幅優先探索を行うメソッド"""

    initPath = [start]
    pathQueue = [initPath]
    while len(pathQueue) != 0:
        tmpPath = pathQueue.pop(0)
        print('Current BFS path:', printPath(tmpPath))
        lastNode = tmpPath[-1]
        if lastNode == end:
            return tmpPath
        for (weight, nextNode) in graph.childrenOf(lastNode):
            if nextNode not in tmpPath:
                newPath = tmpPath + [nextNode]
                pathQueue.append(newPath)

def testSP():
    """テストメソッド"""

    nodes = []
    for name in range(9):
        nodes.append(Node(str(name)))
    g = Digraph()
    for n in nodes:
        g.addNode(n)
    g.addEdge(WeightedEdge(nodes[0], nodes[1], 2))
    g.addEdge(WeightedEdge(nodes[0], nodes[2], 3))
    g.addEdge(WeightedEdge(nodes[0], nodes[3], 4))
    g.addEdge(WeightedEdge(nodes[1], nodes[4], 2))
    g.addEdge(WeightedEdge(nodes[1], nodes[5], 4))
    g.addEdge(WeightedEdge(nodes[2], nodes[5], 1))
    g.addEdge(WeightedEdge(nodes[2], nodes[6], 2))
    g.addEdge(WeightedEdge(nodes[2], nodes[7], 3))
    g.addEdge(WeightedEdge(nodes[3], nodes[8], 6))

    sp = BFS(g, nodes[0], nodes[5])
    print('Shortest path found by BFS:', printPath(sp), '\n')

    sp = UCS(g, nodes[0], nodes[5])
    print('Shortest path found by UCS:', printPath(sp))
testSP()

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def binary_search(lis, val):
    u""" 引数で与えられたListの中から検索valの位置を検索して返す。
    検索値が存在しない場合は-1を返す
    """

    length = len(lis)
    if length == 0:
        return -1

    return _search_array(lis, val,  0, length - 1)

def _search_array(lis, val, start, end):

    mid = int((start + end) / 2)
    if start == end  and lis[mid] != val:
        return -1

    if lis[mid] == val:
        return mid

    elif lis[mid] > val:
        if (start == mid):
            return -1
        return _search_array(lis, val, start, mid - 1)

    else:
        return _search_array(lis, val, mid + 1, end)

    return -1

print(binary_search([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 1))
print(binary_search([1, 3, 5, 8], 4))

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def merge_sort(lis):
    return split_list(lis)

def split_list(arr):
    """
    リストを半分に分ける
    リストの要素が2つになったところで比較を行い、比較した結果をmerge_arrメソッドに渡す。
    """
    if len(arr) <= 1:
        return arr
    elif len(arr) == 2:
        if arr[0] > arr[1]:
            return [arr[1], arr[0]]
        else:
            return arr

    else:
        br = int(len(arr) / 2)
        return merge_arr(split_list(arr[:br]), split_list(arr[br:]))

def merge_arr(arr1, arr2):
    """
    ソート済みの渡されたリストを相互に比較しながらマージする。
    """

    ret = list()
    len1 = len(arr1)
    len2 = len(arr2)

    i = j = 0
    arr1_val = arr1[i]
    arr2_val = arr2[j]

    while len(ret) < len1 + len2:

        if arr1_val < arr2_val:
            ret.append(arr1_val)
            if i < len1 - 1:
                i += 1
                arr1_val = arr1[i]
            else:
                arr1_val = float('inf')

        elif arr1_val >= arr2_val:
            ret.append(arr2_val)
            if j < len2 - 1:
                j += 1
                arr2_val = arr2[j]
            else:
                arr2_val = float('inf')

    return ret

print(merge_sort([]))
print(merge_sort([1]))
print(merge_sort([1, 10, 2, 3, 5, 4, 0]))

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class FugaService:
    def process(self, hoge):
        hoge.echo()


class SuperHoge:
    def echo(self):
        print('super hoge')

class SubHoge(SuperHoge):
    def echo(self):
        print('sub hoge')


service = FugaService()
super_hoge = SuperHoge()
sub_hoge=SubHoge()

service.process(super_hoge)
service.process(sub_hoge)

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class Employee:
    year = 0
    month = 0
    def period(self, year):
        self.year = int(year)
        self.month = int((year - int(year)) * 12)


class Engineer(Employee):
    def period(self, month):
        self.year = int(month / 12)
        self.month = int(month % 12)

def print_exist(employee, year):
    employee.period(year)
    print('在職期間は' , employee.year , '年' , employee.month , 'ヶ月です')

employee = Employee()
engineer = Engineer()

print_exist(employee, 1.5)
print_exist(engineer, 1.5)

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class FugaService:
    def process(self, hoge):
        hoge.echo()


class SuperHoge:
    def echo(self):
        print('super hoge')

class SubHoge(SuperHoge):
    pass

service = FugaService()
super_hoge = SuperHoge()
sub_hoge=SubHoge()

service.process(super_hoge)
service.process(sub_hoge)

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from typing import final

class FugaService:
    def process(self, hoge):
        hoge.echo()


class SuperHoge:
    @final
    def echo(self) -> None:
        print('super hoge')

class SubHoge(SuperHoge):

    def echo(self) -> None:
        print('sub hoge')

service = FugaService()
super_hoge = SuperHoge()
sub_hoge=SubHoge()

service.process(super_hoge)
service.process(sub_hoge)

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❯ mypy hoge.py
hoge.py:15: error: Cannot override final attribute "echo" (previously declared in base class "SuperHoge")
Found 1 error in 1 file (checked 1 source file)