statsmodels-js
Version:
basic statistics library.
2,570 lines (2,503 loc) • 39.8 kB
JavaScript
import {
SimpleLinearRegression,
MultipleLinearRegression,
RidgeRegression,
descripeStats,
pearsonr,
cosSimilarity,
tTest1Sample,
tTestInd,
tTestIndFromStats,
tTestRel,
chiSqaure,
chi2Contingency,
oneWayANOVA,
} from "./index";
import _ from "lodash";
describe("SimpleLinearRegression", () => {
describe("case 1", () => {
const x = [1, 2, 3];
const y = [1, 2, 3];
const result = new SimpleLinearRegression(x, y).fit();
test("coef: 1, intercept: 0", () => {
const coef = result.coef;
const intercept = result.intercept;
expect({ coef, intercept }).toEqual({
coef: 1,
intercept: 0,
});
});
test("predict", () => {
const yPred = result.predict([2, 4, 6]);
expect(yPred).toEqual([2, 4, 6]);
});
test("summary", () => {
expect(result.summary()).toEqual({ coef: 1, intercept: 0, r2Score: 1 });
});
});
describe("case 2", () => {
const x = [0, 3, 6];
const y = [1, 2, 3];
const result = new SimpleLinearRegression(x, y).fit();
test("coef: 1/3, intercept: 1", () => {
const coef = result.coef;
const intercept = result.intercept;
expect({ coef, intercept }).toEqual({
coef: 1 / 3,
intercept: 1,
});
});
test("predict", () => {
const yPred = result.predict([2, 4, 6]);
expect(yPred).toEqual([1.6666666666666665, 2.333333333333333, 3]);
});
test("summary", () => {
expect(result.summary()).toEqual({
coef: 0.3333333333333333,
intercept: 1,
r2Score: 1,
});
});
});
describe("case 3", () => {
const x = [
6.575,
6.421,
7.185,
6.998,
7.147,
6.43,
6.012,
6.172,
5.631,
6.004,
6.377,
6.009,
5.889,
5.949,
6.096,
5.834,
5.935,
5.99,
5.456,
5.727,
5.57,
5.965,
6.142,
5.813,
5.924,
5.599,
5.813,
6.047,
6.495,
6.674,
5.713,
6.072,
5.95,
5.701,
6.096,
5.933,
5.841,
5.85,
5.966,
6.595,
7.024,
6.77,
6.169,
6.211,
6.069,
5.682,
5.786,
6.03,
5.399,
5.602,
5.963,
6.115,
6.511,
5.998,
5.888,
7.249,
6.383,
6.816,
6.145,
5.927,
5.741,
5.966,
6.456,
6.762,
7.104,
6.29,
5.787,
5.878,
5.594,
5.885,
6.417,
5.961,
6.065,
6.245,
6.273,
6.286,
6.279,
6.14,
6.232,
5.874,
6.727,
6.619,
6.302,
6.167,
6.389,
6.63,
6.015,
6.121,
7.007,
7.079,
6.417,
6.405,
6.442,
6.211,
6.249,
6.625,
6.163,
8.069,
7.82,
7.416,
6.727,
6.781,
6.405,
6.137,
6.167,
5.851,
5.836,
6.127,
6.474,
6.229,
6.195,
6.715,
5.913,
6.092,
6.254,
5.928,
6.176,
6.021,
5.872,
5.731,
5.87,
6.004,
5.961,
5.856,
5.879,
5.986,
5.613,
5.693,
6.431,
5.637,
6.458,
6.326,
6.372,
5.822,
5.757,
6.335,
5.942,
6.454,
5.857,
6.151,
6.174,
5.019,
5.403,
5.468,
4.903,
6.13,
5.628,
4.926,
5.186,
5.597,
6.122,
5.404,
5.012,
5.709,
6.129,
6.152,
5.272,
6.943,
6.066,
6.51,
6.25,
7.489,
7.802,
8.375,
5.854,
6.101,
7.929,
5.877,
6.319,
6.402,
5.875,
5.88,
5.572,
6.416,
5.859,
6.546,
6.02,
6.315,
6.86,
6.98,
7.765,
6.144,
7.155,
6.563,
5.604,
6.153,
7.831,
6.782,
6.556,
7.185,
6.951,
6.739,
7.178,
6.8,
6.604,
7.875,
7.287,
7.107,
7.274,
6.975,
7.135,
6.162,
7.61,
7.853,
8.034,
5.891,
6.326,
5.783,
6.064,
5.344,
5.96,
5.404,
5.807,
6.375,
5.412,
6.182,
5.888,
6.642,
5.951,
6.373,
6.951,
6.164,
6.879,
6.618,
8.266,
8.725,
8.04,
7.163,
7.686,
6.552,
5.981,
7.412,
8.337,
8.247,
6.726,
6.086,
6.631,
7.358,
6.481,
6.606,
6.897,
6.095,
6.358,
6.393,
5.593,
5.605,
6.108,
6.226,
6.433,
6.718,
6.487,
6.438,
6.957,
8.259,
6.108,
5.876,
7.454,
8.704,
7.333,
6.842,
7.203,
7.52,
8.398,
7.327,
7.206,
5.56,
7.014,
8.297,
7.47,
5.92,
5.856,
6.24,
6.538,
7.691,
6.758,
6.854,
7.267,
6.826,
6.482,
6.812,
7.82,
6.968,
7.645,
7.923,
7.088,
6.453,
6.23,
6.209,
6.315,
6.565,
6.861,
7.148,
6.63,
6.127,
6.009,
6.678,
6.549,
5.79,
6.345,
7.041,
6.871,
6.59,
6.495,
6.982,
7.236,
6.616,
7.42,
6.849,
6.635,
5.972,
4.973,
6.122,
6.023,
6.266,
6.567,
5.705,
5.914,
5.782,
6.382,
6.113,
6.426,
6.376,
6.041,
5.708,
6.415,
6.431,
6.312,
6.083,
5.868,
6.333,
6.144,
5.706,
6.031,
6.316,
6.31,
6.037,
5.869,
5.895,
6.059,
5.985,
5.968,
7.241,
6.54,
6.696,
6.874,
6.014,
5.898,
6.516,
6.635,
6.939,
6.49,
6.579,
5.884,
6.728,
5.663,
5.936,
6.212,
6.395,
6.127,
6.112,
6.398,
6.251,
5.362,
5.803,
8.78,
3.561,
4.963,
3.863,
4.97,
6.683,
7.016,
6.216,
5.875,
4.906,
4.138,
7.313,
6.649,
6.794,
6.38,
6.223,
6.968,
6.545,
5.536,
5.52,
4.368,
5.277,
4.652,
5,
4.88,
5.39,
5.713,
6.051,
5.036,
6.193,
5.887,
6.471,
6.405,
5.747,
5.453,
5.852,
5.987,
6.343,
6.404,
5.349,
5.531,
5.683,
4.138,
5.608,
5.617,
6.852,
5.757,
6.657,
4.628,
5.155,
4.519,
6.434,
6.782,
5.304,
5.957,
6.824,
6.411,
6.006,
5.648,
6.103,
5.565,
5.896,
5.837,
6.202,
6.193,
6.38,
6.348,
6.833,
6.425,
6.436,
6.208,
6.629,
6.461,
6.152,
5.935,
5.627,
5.818,
6.406,
6.219,
6.485,
5.854,
6.459,
6.341,
6.251,
6.185,
6.417,
6.749,
6.655,
6.297,
7.393,
6.728,
6.525,
5.976,
5.936,
6.301,
6.081,
6.701,
6.376,
6.317,
6.513,
6.209,
5.759,
5.952,
6.003,
5.926,
5.713,
6.167,
6.229,
6.437,
6.98,
5.427,
6.162,
6.484,
5.304,
6.185,
6.229,
6.242,
6.75,
7.061,
5.762,
5.871,
6.312,
6.114,
5.905,
5.454,
5.414,
5.093,
5.983,
5.983,
5.707,
5.926,
5.67,
5.39,
5.794,
6.019,
5.569,
6.027,
6.593,
6.12,
6.976,
6.794,
6.03,
];
const y = [
24,
21.6,
34.7,
33.4,
36.2,
28.7,
22.9,
27.1,
16.5,
18.9,
15,
18.9,
21.7,
20.4,
18.2,
19.9,
23.1,
17.5,
20.2,
18.2,
13.6,
19.6,
15.2,
14.5,
15.6,
13.9,
16.6,
14.8,
18.4,
21,
12.7,
14.5,
13.2,
13.1,
13.5,
18.9,
20,
21,
24.7,
30.8,
34.9,
26.6,
25.3,
24.7,
21.2,
19.3,
20,
16.6,
14.4,
19.4,
19.7,
20.5,
25,
23.4,
18.9,
35.4,
24.7,
31.6,
23.3,
19.6,
18.7,
16,
22.2,
25,
33,
23.5,
19.4,
22,
17.4,
20.9,
24.2,
21.7,
22.8,
23.4,
24.1,
21.4,
20,
20.8,
21.2,
20.3,
28,
23.9,
24.8,
22.9,
23.9,
26.6,
22.5,
22.2,
23.6,
28.7,
22.6,
22,
22.9,
25,
20.6,
28.4,
21.4,
38.7,
43.8,
33.2,
27.5,
26.5,
18.6,
19.3,
20.1,
19.5,
19.5,
20.4,
19.8,
19.4,
21.7,
22.8,
18.8,
18.7,
18.5,
18.3,
21.2,
19.2,
20.4,
19.3,
22,
20.3,
20.5,
17.3,
18.8,
21.4,
15.7,
16.2,
18,
14.3,
19.2,
19.6,
23,
18.4,
15.6,
18.1,
17.4,
17.1,
13.3,
17.8,
14,
14.4,
13.4,
15.6,
11.8,
13.8,
15.6,
14.6,
17.8,
15.4,
21.5,
19.6,
15.3,
19.4,
17,
15.6,
13.1,
41.3,
24.3,
23.3,
27,
50,
50,
50,
22.7,
25,
50,
23.8,
23.8,
22.3,
17.4,
19.1,
23.1,
23.6,
22.6,
29.4,
23.2,
24.6,
29.9,
37.2,
39.8,
36.2,
37.9,
32.5,
26.4,
29.6,
50,
32,
29.8,
34.9,
37,
30.5,
36.4,
31.1,
29.1,
50,
33.3,
30.3,
34.6,
34.9,
32.9,
24.1,
42.3,
48.5,
50,
22.6,
24.4,
22.5,
24.4,
20,
21.7,
19.3,
22.4,
28.1,
23.7,
25,
23.3,
28.7,
21.5,
23,
26.7,
21.7,
27.5,
30.1,
44.8,
50,
37.6,
31.6,
46.7,
31.5,
24.3,
31.7,
41.7,
48.3,
29,
24,
25.1,
31.5,
23.7,
23.3,
22,
20.1,
22.2,
23.7,
17.6,
18.5,
24.3,
20.5,
24.5,
26.2,
24.4,
24.8,
29.6,
42.8,
21.9,
20.9,
44,
50,
36,
30.1,
33.8,
43.1,
48.8,
31,
36.5,
22.8,
30.7,
50,
43.5,
20.7,
21.1,
25.2,
24.4,
35.2,
32.4,
32,
33.2,
33.1,
29.1,
35.1,
45.4,
35.4,
46,
50,
32.2,
22,
20.1,
23.2,
22.3,
24.8,
28.5,
37.3,
27.9,
23.9,
21.7,
28.6,
27.1,
20.3,
22.5,
29,
24.8,
22,
26.4,
33.1,
36.1,
28.4,
33.4,
28.2,
22.8,
20.3,
16.1,
22.1,
19.4,
21.6,
23.8,
16.2,
17.8,
19.8,
23.1,
21,
23.8,
23.1,
20.4,
18.5,
25,
24.6,
23,
22.2,
19.3,
22.6,
19.8,
17.1,
19.4,
22.2,
20.7,
21.1,
19.5,
18.5,
20.6,
19,
18.7,
32.7,
16.5,
23.9,
31.2,
17.5,
17.2,
23.1,
24.5,
26.6,
22.9,
24.1,
18.6,
30.1,
18.2,
20.6,
17.8,
21.7,
22.7,
22.6,
25,
19.9,
20.8,
16.8,
21.9,
27.5,
21.9,
23.1,
50,
50,
50,
50,
50,
13.8,
13.8,
15,
13.9,
13.3,
13.1,
10.2,
10.4,
10.9,
11.3,
12.3,
8.8,
7.2,
10.5,
7.4,
10.2,
11.5,
15.1,
23.2,
9.7,
13.8,
12.7,
13.1,
12.5,
8.5,
5,
6.3,
5.6,
7.2,
12.1,
8.3,
8.5,
5,
11.9,
27.9,
17.2,
27.5,
15,
17.2,
17.9,
16.3,
7,
7.2,
7.5,
10.4,
8.8,
8.4,
16.7,
14.2,
20.8,
13.4,
11.7,
8.3,
10.2,
10.9,
11,
9.5,
14.5,
14.1,
16.1,
14.3,
11.7,
13.4,
9.6,
8.7,
8.4,
12.8,
10.5,
17.1,
18.4,
15.4,
10.8,
11.8,
14.9,
12.6,
14.1,
13,
13.4,
15.2,
16.1,
17.8,
14.9,
14.1,
12.7,
13.5,
14.9,
20,
16.4,
17.7,
19.5,
20.2,
21.4,
19.9,
19,
19.1,
19.1,
20.1,
19.9,
19.6,
23.2,
29.8,
13.8,
13.3,
16.7,
12,
14.6,
21.4,
23,
23.7,
25,
21.8,
20.6,
21.2,
19.1,
20.6,
15.2,
7,
8.1,
13.6,
20.1,
21.8,
24.5,
23.1,
19.7,
18.3,
21.2,
17.5,
16.8,
22.4,
20.6,
23.9,
22,
11.9,
];
const actual = new SimpleLinearRegression(x, y).fit();
const coef = _.ceil(actual.coef, 7);
const intercept = _.ceil(actual.intercept, 7);
test("boston data coef = 9.1021089 intercept = -34.6706207764", () => {
/*
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
x = boston_df['RM'].values # 説明変数（Numpyの配列）
y = boston_df['MEDV'].values
*/
expect({ coef, intercept }).toEqual({
coef: _.ceil(9.10210898118031, 7), // output by sklearn
intercept: _.ceil(-34.67062077643857, 7),
});
});
});
});
describe("MultipleLinearRegression", () => {
describe("multi-colinearlity", () => {
test("exists", () => {
const x = [
[],
[],
[],
];
const y = [2, 3, 6];
const mlr = new MultipleLinearRegression(x, y);
expect(mlr._hasMultiCo).toEqual(true);
});
test("doesn't exist", () => {
const x = [
[],
[],
[],
];
const y = [2, 3, 6];
const mlr = new MultipleLinearRegression(x, y);
expect(mlr._hasMultiCo).toEqual(false);
});
});
describe("multi-colinearlity, every variable should have coefficients", () => {
test("case 1", () => {
const x = [
[],
[],
[],
];
const y = [2, 3, 6];
const result = new MultipleLinearRegression(x, y).fit();
const expected = [[-0.3333333333333333], [1], [1]];
expect(result._w).toEqual(expected);
});
test("case 2", () => {
const x = [
[],
[],
[],
];
const y = [2, 3, 3];
const result = new MultipleLinearRegression(x, y).fit();
const expected = [
[],
[],
[],
[],
];
expect(result._w).toEqual(expected);
});
});
test("not multi-colinearlity", () => {
const x = [
[],
[],
[],
];
const y = [2, 3, 6];
const result = new MultipleLinearRegression(x, y).fit();
const expected = [[1], [3], [-2]];
expect(result._w).toEqual(expected);
});
describe("predict", () => {
test("", () => {
const x = [
[],
[],
[],
];
const y = [1, 2, 3];
const predictX = [10, 20, 30];
const reg = new MultipleLinearRegression(x, y);
reg.fit();
const expected = [[0.9999999999999787]];
expect(reg.predict(predictX)).toEqual(expected);
});
test("", () => {
const x = [
[],
[],
[],
];
const y = [1, 2, 3];
const predictX = [
[],
[],
];
const reg = new MultipleLinearRegression(x, y);
reg.fit();
const expected = [[0.9999999999999787], [2.000000000000007]];
expect(reg.predict(predictX)).toEqual(expected);
});
test("input size error", () => {
const x = [
[],
[],
[],
];
const y = [1, 2, 3];
const predictX = [
[],
[],
];
const reg = new MultipleLinearRegression(x, y);
reg.fit();
expect(() => {
reg.predict(predictX);
}).toThrowError(new Error("The number of variables should be 3"));
});
});
describe("summary", () => {
test("", () => {
const x = [
[],
[],
[],
];
const y = [1, 2, 3];
const reg = new MultipleLinearRegression(x, y).fit();
const expected = {
intercept: 0.3717447916666898,
r2Score: 1,
x1: -0.3089192708334065,
x2: -0.7054850260416322,
x3: 0.59423828125,
};
expect(reg.summary()).toEqual(expected);
});
});
});
describe("RidgeRegression", () => {
describe("case 1", () => {
const x = [[4], [9], [12]];
const y = [1, 2, 3];
const reg = new RidgeRegression(x, y).fit();
test("_w", () => {
const expected = [[0.005830903790087803], [0.23906705539358597]];
expect(reg._w).toEqual(expected);
});
test("predict()", () => {
const a = [[1], [2]];
const expected = [[0.24489795918367377], [0.48396501457725977]];
expect(reg.predict(a)).toEqual(expected);
});
});
describe("boston data", () => {
const x = [
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
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[],
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[],
[],
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[],
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[],
[],
[],
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[],
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[],
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[],
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[],
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[],
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[],
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[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
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[],
[],
[],
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[],
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[],
[],
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[],
[],
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[],
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[],
[],
[],
[],
[],
[],
[],
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[],
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[],
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[],
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[],
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[],
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[],
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[],
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[],
[],
[],
];
const y = [
24,
21.6,
34.7,
33.4,
36.2,
28.7,
22.9,
27.1,
16.5,
18.9,
15,
18.9,
21.7,
20.4,
18.2,
19.9,
23.1,
17.5,
20.2,
18.2,
13.6,
19.6,
15.2,
14.5,
15.6,
13.9,
16.6,
14.8,
18.4,
21,
12.7,
14.5,
13.2,
13.1,
13.5,
18.9,
20,
21,
24.7,
30.8,
34.9,
26.6,
25.3,
24.7,
21.2,
19.3,
20,
16.6,
14.4,
19.4,
19.7,
20.5,
25,
23.4,
18.9,
35.4,
24.7,
31.6,
23.3,
19.6,
18.7,
16,
22.2,
25,
33,
23.5,
19.4,
22,
17.4,
20.9,
24.2,
21.7,
22.8,
23.4,
24.1,
21.4,
20,
20.8,
21.2,
20.3,
28,
23.9,
24.8,
22.9,
23.9,
26.6,
22.5,
22.2,
23.6,
28.7,
22.6,
22,
22.9,
25,
20.6,
28.4,
21.4,
38.7,
43.8,
33.2,
27.5,
26.5,
18.6,
19.3,
20.1,
19.5,
19.5,
20.4,
19.8,
19.4,
21.7,
22.8,
18.8,
18.7,
18.5,
18.3,
21.2,
19.2,
20.4,
19.3,
22,
20.3,
20.5,
17.3,
18.8,
21.4,
15.7,
16.2,
18,
14.3,
19.2,
19.6,
23,
18.4,
15.6,
18.1,
17.4,
17.1,
13.3,
17.8,
14,
14.4,
13.4,
15.6,
11.8,
13.8,
15.6,
14.6,
17.8,
15.4,
21.5,
19.6,
15.3,
19.4,
17,
15.6,
13.1,
41.3,
24.3,
23.3,
27,
50,
50,
50,
22.7,
25,
50,
23.8,
23.8,
22.3,
17.4,
19.1,
23.1,
23.6,
22.6,
29.4,
23.2,
24.6,
29.9,
37.2,
39.8,
36.2,
37.9,
32.5,
26.4,
29.6,
50,
32,
29.8,
34.9,
37,
30.5,
36.4,
31.1,
29.1,
50,
33.3,
30.3,
34.6,
34.9,
32.9,
24.1,
42.3,
48.5,
50,
22.6,
24.4,
22.5,
24.4,
20,
21.7,
19.3,
22.4,
28.1,
23.7,
25,
23.3,
28.7,
21.5,
23,
26.7,
21.7,
27.5,
30.1,
44.8,
50,
37.6,
31.6,
46.7,
31.5,
24.3,
31.7,
41.7,
48.3,
29,
24,
25.1,
31.5,
23.7,
23.3,
22,
20.1,
22.2,
23.7,
17.6,
18.5,
24.3,
20.5,
24.5,
26.2,
24.4,
24.8,
29.6,
42.8,
21.9,
20.9,
44,
50,
36,
30.1,
33.8,
43.1,
48.8,
31,
36.5,
22.8,
30.7,
50,
43.5,
20.7,
21.1,
25.2,
24.4,
35.2,
32.4,
32,
33.2,
33.1,
29.1,
35.1,
45.4,
35.4,
46,
50,
32.2,
22,
20.1,
23.2,
22.3,
24.8,
28.5,
37.3,
27.9,
23.9,
21.7,
28.6,
27.1,
20.3,
22.5,
29,
24.8,
22,
26.4,
33.1,
36.1,
28.4,
33.4,
28.2,
22.8,
20.3,
16.1,
22.1,
19.4,
21.6,
23.8,
16.2,
17.8,
19.8,
23.1,
21,
23.8,
23.1,
20.4,
18.5,
25,
24.6,
23,
22.2,
19.3,
22.6,
19.8,
17.1,
19.4,
22.2,
20.7,
21.1,
19.5,
18.5,
20.6,
19,
18.7,
32.7,
16.5,
23.9,
31.2,
17.5,
17.2,
23.1,
24.5,
26.6,
22.9,
24.1,
18.6,
30.1,
18.2,
20.6,
17.8,
21.7,
22.7,
22.6,
25,
19.9,
20.8,
16.8,
21.9,
27.5,
21.9,
23.1,
50,
50,
50,
50,
50,
13.8,
13.8,
15,
13.9,
13.3,
13.1,
10.2,
10.4,
10.9,
11.3,
12.3,
8.8,
7.2,
10.5,
7.4,
10.2,
11.5,
15.1,
23.2,
9.7,
13.8,
12.7,
13.1,
12.5,
8.5,
5,
6.3,
5.6,
7.2,
12.1,
8.3,
8.5,
5,
11.9,
27.9,
17.2,
27.5,
15,
17.2,
17.9,
16.3,
7,
7.2,
7.5,
10.4,
8.8,
8.4,
16.7,
14.2,
20.8,
13.4,
11.7,
8.3,
10.2,
10.9,
11,
9.5,
14.5,
14.1,
16.1,
14.3,
11.7,
13.4,
9.6,
8.7,
8.4,
12.8,
10.5,
17.1,
18.4,
15.4,
10.8,
11.8,
14.9,
12.6,
14.1,
13,
13.4,
15.2,
16.1,
17.8,
14.9,
14.1,
12.7,
13.5,
14.9,
20,
16.4,
17.7,
19.5,
20.2,
21.4,
19.9,
19,
19.1,
19.1,
20.1,
19.9,
19.6,
23.2,
29.8,
13.8,
13.3,
16.7,
12,
14.6,
21.4,
23,
23.7,
25,
21.8,
20.6,
21.2,
19.1,
20.6,
15.2,
7,
8.1,
13.6,
20.1,
21.8,
24.5,
23.1,
19.7,
18.3,
21.2,
17.5,
16.8,
22.4,
20.6,
23.9,
22,
11.9,
];
const reg = new RidgeRegression(x, y, 0.5).fit();
test("", () => {
/*
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
x = boston_df['RM'].values # 説明変数（Numpyの配列）
y = boston_df['MEDV'].values
*/
const expected = [[-31.995123945883126], [8.68141925883909]];
expect(reg._w).toEqual(expected);
});
});
});
describe("describeStats", () => {
/*
import scipy
scipy.stats.ttest_1samp([5,5,5,5,5,5,6,10],5.0)
# Ttest_1sampResult(statistic=1.2104198771788934, pvalue=0.2653980394260696)
*/
describe("[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]", () => {
// np.mean([5, 5, 5, 5, 5, 5, 6, 10])
const result = descripeStats([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]);
test("size", () => {
expect(result.size).toEqual(10);
});
test("min", () => {
expect(result.min).toEqual(0);
});
test("max", () => {
expect(result.max).toEqual(9);
});
test("mean", () => {
expect(result.mean).toEqual(4.5);
});
test("variance", () => {
expect(result.variance).toEqual(9.166666666666666);
});
test("skewness", () => {
expect(result.skewness).toEqual(0);
});
test("kurtosis", () => {
expect(result.kurtosis).toEqual(-1.2);
});
});
});
describe("consSimilarity", () => {
describe("", () => {
test("", () => {
const a = [1, 1];
const b = [0, 1];
expect(cosSimilarity(a, b)).toEqual(0.7071067811865475);
});
test("", () => {
const a = [1, 0];
const b = [0, 1];
expect(cosSimilarity(a, b)).toEqual(0);
});
test("", () => {
const a = [1, 1];
const b = [1, 1];
expect(cosSimilarity(a, b)).toEqual(1);
});
});
});
describe("pearsonr", () => {
describe("case 1", () => {
const a = [0, 0, 0, 1, 1, 1, 1];
const b = [0, 1, 2, 3, 4, 5, 6];
const result = pearsonr(a, b);
test("r", () => {
expect(result.r).toEqual(0.8660254037844386);
});
test("pValue", () => {
expect(result.pValue).toEqual(0.011724811003882547);
});
});
describe("case 2", () => {
const a = [2, 3, 5, 6, 3, 4, 7, 5];
const b = [4, 3, 8, 7, 5, 4, 8, 9];
const result = pearsonr(a, b);
test("r", () => {
expect(result.r).toEqual(0.785081015728862);
});
test("pValue", () => {
expect(result.pValue).toEqual(0.020989440024479375);
});
});
});
describe("tTest1Sample", () => {
/*
import scipy
scipy.stats.ttest_1samp([5,5,5,5,5,5,6,10],5.0)
# Ttest_1sampResult(statistic=1.2104198771788934, pvalue=0.2653980394260696)
*/
describe("([5, 5, 5, 5, 5, 5, 6, 10], 5.0)", () => {
// np.mean([5, 5, 5, 5, 5, 5, 6, 10])
const result = tTest1Sample([5, 5, 5, 5, 5, 5, 6, 10], 5.0);
test("mean", () => {
expect(result.mean).toEqual(5.75);
});
test("sd", () => {
expect(result.sd).toEqual(1.6393596310755);
});
test("result", () => {
expect(result.statistic).toEqual(1.2104198771788937);
expect(result.pValue).toEqual(0.26539803962501435);
});
});
});
describe("tTestInd", () => {
/*
import scipy
scipy.stats.ttest_ind([4,5,6,4,5], [1,2,3,4,5], equal_var=True)
# Ttest_indResult(statistic=2.2499999999999996, pvalue=0.05456730579993522)
*/
describe("[4,5,6,4,5], [1,2,3,4,5], equalVar true", () => {
// np.mean([5, 5, 5, 5, 5, 5, 6, 10])
const result = tTestInd([4, 5, 6, 4, 5], [1, 2, 3, 4, 5], true);
test("statistic", () => {
expect(result.statistic).toEqual(2.2499999999999996);
});
test("pValue", () => {
expect(result.pValue).toEqual(0.054567305799939875);
});
test("se", () => {
expect(result.se).toEqual(1.2649110640673518);
});
test("df", () => {
expect(result.df).toEqual(8);
});
});
/*
import scipy
scipy.stats.ttest_ind([4,5,6,4,5], [1,2,3,4,5], equal_var=False)
# Ttest_indResult(statistic=2.2499999999999996, pvalue=0.06488370852883803)
*/
describe("[4,5,6,4,5], [1,2,3,4,5], equalVar false", () => {
// np.mean([5, 5, 5, 5, 5, 5, 6, 10])
const result = tTestInd([4, 5, 6, 4, 5], [1, 2, 3, 4, 5], false);
test("statistic", () => {
expect(result.statistic).toEqual(2.2499999999999996);
});
test("pValue", () => {
expect(result.pValue).toEqual(0.06488370852885418);
});
test("se", () => {
expect(result.se).toEqual(0.8);
});
test("df", () => {
expect(result.df).toEqual(6.077151335311573);
});
});
});
describe("tTestIndFromStats", () => {
/*
import scipy
ttest_ind_from_stats(mean1=0.2, std1=np.sqrt(0.16), nobs1=150,
... mean2=0.225, std2=np.sqrt(0.17437), nobs2=200)
# Ttest_indResult(statistic=-0.564327545549774, pvalue=0.5728947691244874)
*/
describe("equalVar true", () => {
const result = tTestIndFromStats(
0.2,
Math.sqrt(0.16),
150,
0.225,
Math.sqrt(0.17437),
200,
true
);
test("statistic", () => {
expect(result.statistic).toEqual(-0.564327545549774);
});
test("pValue", () => {
expect(result.pValue).toEqual(0.572894755162566);
});
});
});
describe("tTestRel", () => {
/*
import scipy
scipy.stats.ttest_rel([4,5,6,4,5], [1,2,3,4,5])
# Ttest_relResult(statistic=2.449489742783178, pvalue=0.07048399691021993)
*/
describe("", () => {
const result = tTestRel([4, 5, 6, 4, 5], [1, 2, 3, 4, 5]);
test("statistic", () => {
expect(result.statistic).toEqual(2.449489742783178);
});
test("pValue", () => {
expect(result.pValue).toEqual(0.07048399691022006);
});
});
});
describe("goodness-of-fit test", () => {
test("[10,1,1], [10,1,1]", () => {
const result = chiSqaure([10, 1, 1], [10, 1, 1]);
expect(result.statistic).toEqual(0);
expect(result.pValue).toEqual(1);
});
/*
import scipy
a = scipy.array([10,1,1,1])
b = scipy.array([15,1,1,1])
scipy.stats.chisquare(a, f_exp=b)
*/
test("[10,1,1,1], [15,1,1,1]", () => {
const result = chiSqaure([10, 1, 1, 1], [15, 1, 1, 1]);
expect(result.statistic).toEqual(1.6666666666666667);
expect(result.pValue).toEqual(0.6443698056370236);
});
});
describe("test for independence", () => {
/*
import scipy
scipy.stats.chi2_contingency(np.array([[55, 22, 16, 7], [40, 32, 24, 4]]))
(6.63845472266525, 0.08435923449835014, 3, array([[47.5, 27. , 20. , 5.5],
[47.5, 27. , 20. , 5.5]]))
*/
test("[55, 22, 16, 7], [40, 32, 24, 4]", () => {
const result = chi2Contingency([55, 22, 16, 7], [40, 32, 24, 4]);
expect(result.statistic).toEqual(6.63845472266525);
expect(result.pValue).toEqual(0.08435923449835192);
});
});
describe("oneWayANOVA", () => {
/*
import scipy
a = np.array([66, 62, 80, 50, 57, 68, 73, 65])
b = np.array([62, 60, 66, 63, 55, 53, 59, 63])
c = np.array([65, 60, 78, 52, 59, 66, 73, 64])
d = np.array([52, 59, 44, 67, 47, 53, 58, 49])
scipy.stats.f_oneway(a,b,c,d)
# F_onewayResult(statistic=4.024870903151017, pvalue=0.016859897941251333)
*/
test("[55, 22, 16, 7], [40, 32, 24, 4]", () => {
const a = [66, 62, 80, 50, 57, 68, 73, 65];
const b = [62, 60, 66, 63, 55, 53, 59, 63];
const c = [65, 60, 78, 52, 59, 66, 73, 64];
const d = [52, 59, 44, 67, 47, 53, 58, 49];
const result = oneWayANOVA(a, b, c, d);
expect(result.statistic).toEqual(4.024870903151017);
expect(result.pValue).toEqual(0.01685989800789034);
});
});