python做频率统计图完整版

一、效果二、代码设计# your code goes herefrom matplotlib import pyplot as pltimport pandas as pddef linearCongruentialMethod(Xo, m, a, c, randomNums, U):randomNums[0] = XoU[0] = randomNums[0] / mfor i in range(

陈海明hack

1366人浏览 · 2021-08-10 21:35:05

陈海明hack · 2021-08-10 21:35:05 发布

一、效果

二、代码设计

# your code goes here
from matplotlib import pyplot as plt
import pandas as pd


def linearCongruentialMethod(Xo, m, a, c, randomNums, U):
    randomNums[0] = Xo
    U[0] = randomNums[0] / m
    for i in range(1, 10000):
        # Follow the linear congruential method
        randomNums[i] = ((randomNums[i - 1] * a) + c) % m
        U[i] = randomNums[i] / m


print("a = 1597, b = 0, m = 244944")
a = 1597
c = 0
m = 244944
i = 0.01
for i in range(1, 6):
    X0 = i * 0.01
    print("\n X0 = ", X0, "\n")
    noOfRandomNums = 10005
    randomNums = [0] * (noOfRandomNums)
    U = [0] * (noOfRandomNums)
    linearCongruentialMethod(X0, m, a, c, randomNums, U)

    intervals = 20
    freq = [0] * (intervals)

    for j in U:
        x = 1
        x = j * 100 / 5
        freq[int(x)] = freq[int(x)] + 1

    mydata = {'Interval  ': ['[0.00,0.05)', '[0.05,0.10)', '[0.10,0.15)', '[0.15,0.20)', '[0.20,0.25)', '[0.25,0.30)',
                             '[0.30,0.35)', '[0.35,0.40)', '[0.40,0.45)', '[0.45,0.50)', '[0.50,0.55)', '[0.55,0.60)',
                             '[0.60,0.65)', '[0.65,0.70)', '[0.70,0.75)', '[0.75,0.80)', '[0.80,0.85)', '[0.85,0.90)',
                             '[0.90,0.95)', '[0.95,1.00)'],
              'Freuency': freq}
    df = pd.DataFrame(mydata)
    print(df)

    data = {'1': freq[0], '2': freq[1], '3': freq[2], '4': freq[3], '5': freq[4], '6': freq[5], '7': freq[6],
            '8': freq[7], '9': freq[8], '10': freq[9], '11': freq[10], '12': freq[11], '13': freq[12], '14': freq[13],
            '15': freq[14], '16': freq[15], '17': freq[16], '18': freq[17], '19': freq[18], '20': freq[19], }
    interval = list(data.keys())
    frequency = list(data.values())
    fig = plt.figure(figsize=(10, 5))
    plt.bar(interval, frequency, color='maroon', width=0.4)
    plt.show()
print("\na = 51749, b = 0, m = 244944")
a = 51749
c = 0
m = 244944
i = 0.01
for i in range(1, 6):
    X0 = i * 0.01
    print("\n X0 = ", X0, "\n")
    noOfRandomNums = 10005
    randomNums = [0] * (noOfRandomNums)
    U = [0] * (noOfRandomNums)
    linearCongruentialMethod(X0, m, a, c, randomNums, U)

    intervals = 20
    freq = [0] * (intervals)

    for j in U:
        x = 1
        x = j * 100 / 5
        freq[int(x)] = freq[int(x)] + 1

    mydata = {'Interval  ': ['[0.00,0.05)', '[0.05,0.10)', '[0.10,0.15)', '[0.15,0.20)', '[0.20,0.25)', '[0.25,0.30)',
                             '[0.30,0.35)', '[0.35,0.40)', '[0.40,0.45)', '[0.45,0.50)', '[0.50,0.55)', '[0.55,0.60)',
                             '[0.60,0.65)', '[0.65,0.70)', '[0.70,0.75)', '[0.75,0.80)', '[0.80,0.85)', '[0.85,0.90)',
                             '[0.90,0.95)', '[0.95,1.00)'],
              'Freuency': freq}
    df = pd.DataFrame(mydata)
    print(df)

    data = {'1': freq[0], '2': freq[1], '3': freq[2], '4': freq[3], '5': freq[4], '6': freq[5], '7': freq[6],
            '8': freq[7], '9': freq[8], '10': freq[9], '11': freq[10], '12': freq[11], '13': freq[12], '14': freq[13],
            '15': freq[14], '16': freq[15], '17': freq[16], '18': freq[17], '19': freq[18], '20': freq[19], }
    interval = list(data.keys())
    frequency = list(data.values())
    fig = plt.figure(figsize=(10, 5))
    plt.bar(interval, frequency, color='blue', width=0.4)
    plt.show()

三、数据区间和频率

控制台记录：

a = 1597, b = 0, m = 244944

X0 = 0.01

Interval Freuency
0 [0.00,0.05) 518
1 [0.05,0.10) 504
2 [0.10,0.15) 486
3 [0.15,0.20) 490
4 [0.20,0.25) 506
5 [0.25,0.30) 512
6 [0.30,0.35) 498
7 [0.35,0.40) 480
8 [0.40,0.45) 515
9 [0.45,0.50) 490
10 [0.50,0.55) 457
11 [0.55,0.60) 550
12 [0.60,0.65) 517
13 [0.65,0.70) 504
14 [0.70,0.75) 504
15 [0.75,0.80) 490
16 [0.80,0.85) 469
17 [0.85,0.90) 476
18 [0.90,0.95) 518
19 [0.95,1.00) 521

X0 = 0.02

Interval Freuency
0 [0.00,0.05) 496
1 [0.05,0.10) 479
2 [0.10,0.15) 526
3 [0.15,0.20) 528
4 [0.20,0.25) 499
5 [0.25,0.30) 504
6 [0.30,0.35) 488
7 [0.35,0.40) 506
8 [0.40,0.45) 491
9 [0.45,0.50) 519
10 [0.50,0.55) 496
11 [0.55,0.60) 506
12 [0.60,0.65) 489
13 [0.65,0.70) 478
14 [0.70,0.75) 480
15 [0.75,0.80) 476
16 [0.80,0.85) 502
17 [0.85,0.90) 531
18 [0.90,0.95) 512
19 [0.95,1.00) 499

X0 = 0.03

Interval Freuency
0 [0.00,0.05) 537
1 [0.05,0.10) 517
2 [0.10,0.15) 462
3 [0.15,0.20) 476
4 [0.20,0.25) 506
5 [0.25,0.30) 498
6 [0.30,0.35) 492
7 [0.35,0.40) 505
8 [0.40,0.45) 512
9 [0.45,0.50) 506
10 [0.50,0.55) 493
11 [0.55,0.60) 508
12 [0.60,0.65) 529
13 [0.65,0.70) 508
14 [0.70,0.75) 489
15 [0.75,0.80) 503
16 [0.80,0.85) 483
17 [0.85,0.90) 504
18 [0.90,0.95) 468
19 [0.95,1.00) 509

X0 = 0.04

Interval Freuency
0 [0.00,0.05) 522
1 [0.05,0.10) 486
2 [0.10,0.15) 486
3 [0.15,0.20) 497
4 [0.20,0.25) 486
5 [0.25,0.30) 501
6 [0.30,0.35) 500
7 [0.35,0.40) 499
8 [0.40,0.45) 534
9 [0.45,0.50) 528
10 [0.50,0.55) 539
11 [0.55,0.60) 502
12 [0.60,0.65) 454
13 [0.65,0.70) 478
14 [0.70,0.75) 511
15 [0.75,0.80) 496
16 [0.80,0.85) 481
17 [0.85,0.90) 485
18 [0.90,0.95) 514
19 [0.95,1.00) 506