| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2017 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | import * as tf from '../index';
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| 18 | import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
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| 19 | import { expectArraysClose } from '../test_util';
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| 20 | describeWithFlags('multinomial', ALL_ENVS, () => {
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| 21 | const NUM_SAMPLES = 1000;
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| 22 | // Allowed Variance in probability (in %).
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| 23 | const EPSILON = 0.05;
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| 24 | const SEED = 3.14;
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| 25 | it('Flip a fair coin and check bounds', async () => {
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| 26 | const probs = tf.tensor1d([1, 1]);
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| 27 | const result = tf.multinomial(probs, NUM_SAMPLES, SEED);
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| 28 | expect(result.dtype).toBe('int32');
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| 29 | expect(result.shape).toEqual([NUM_SAMPLES]);
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| 30 | const outcomeProbs = computeProbs(await result.data(), 2);
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| 31 | expectArraysClose(outcomeProbs, [0.5, 0.5], EPSILON);
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| 32 | });
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| 33 | it('Flip a two-sided coin with 100% of heads', async () => {
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| 34 | const logits = tf.tensor1d([1, -100]);
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| 35 | const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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| 36 | expect(result.dtype).toBe('int32');
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| 37 | expect(result.shape).toEqual([NUM_SAMPLES]);
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| 38 | const outcomeProbs = computeProbs(await result.data(), 2);
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| 39 | expectArraysClose(outcomeProbs, [1, 0], EPSILON);
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| 40 | });
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| 41 | it('Flip a two-sided coin with 100% of tails', async () => {
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| 42 | const logits = tf.tensor1d([-100, 1]);
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| 43 | const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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| 44 | expect(result.dtype).toBe('int32');
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| 45 | expect(result.shape).toEqual([NUM_SAMPLES]);
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| 46 | const outcomeProbs = computeProbs(await result.data(), 2);
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| 47 | expectArraysClose(outcomeProbs, [0, 1], EPSILON);
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| 48 | });
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| 49 | it('Flip a single-sided coin throws error', () => {
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| 50 | const probs = tf.tensor1d([1]);
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| 51 | expect(() => tf.multinomial(probs, NUM_SAMPLES, SEED)).toThrowError();
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| 52 | });
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| 53 | it('Flip a ten-sided coin and check bounds', async () => {
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| 54 | const numOutcomes = 10;
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| 55 | const logits = tf.fill([numOutcomes], 1).as1D();
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| 56 | const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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| 57 | expect(result.dtype).toBe('int32');
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| 58 | expect(result.shape).toEqual([NUM_SAMPLES]);
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| 59 | const outcomeProbs = computeProbs(await result.data(), numOutcomes);
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| 60 | expect(outcomeProbs.length).toBeLessThanOrEqual(numOutcomes);
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| 61 | });
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| 62 | it('Flip 3 three-sided coins, each coin is 100% biases', async () => {
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| 63 | const numOutcomes = 3;
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| 64 | const logits = tf.tensor2d([[-100, -100, 1], [-100, 1, -100], [1, -100, -100]], [3, numOutcomes]);
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| 65 | const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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| 66 | expect(result.dtype).toBe('int32');
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| 67 | expect(result.shape).toEqual([3, NUM_SAMPLES]);
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| 68 | // First coin always gets last event.
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| 69 | let outcomeProbs = computeProbs((await result.data()).slice(0, NUM_SAMPLES), numOutcomes);
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| 70 | expectArraysClose(outcomeProbs, [0, 0, 1], EPSILON);
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| 71 | // Second coin always gets middle event.
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| 72 | outcomeProbs = computeProbs((await result.data()).slice(NUM_SAMPLES, 2 * NUM_SAMPLES), numOutcomes);
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| 73 | expectArraysClose(outcomeProbs, [0, 1, 0], EPSILON);
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| 74 | // Third coin always gets first event
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| 75 | outcomeProbs =
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| 76 | computeProbs((await result.data()).slice(2 * NUM_SAMPLES), numOutcomes);
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| 77 | expectArraysClose(outcomeProbs, [1, 0, 0], EPSILON);
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| 78 | });
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| 79 | it('passing Tensor3D throws error', () => {
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| 80 | const probs = tf.zeros([3, 2, 2]);
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| 81 | const normalized = true;
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| 82 | expect(() => tf.multinomial(probs, 3, SEED, normalized))
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| 83 | .toThrowError();
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| 84 | });
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| 85 | it('throws when passed a non-tensor', () => {
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| 86 | // tslint:disable-next-line:no-any
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| 87 | expect(() => tf.multinomial({}, NUM_SAMPLES, SEED))
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| 88 | .toThrowError(/Argument 'logits' passed to 'multinomial' must be a Tensor/);
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| 89 | });
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| 90 | it('accepts a tensor-like object for logits (biased coin)', async () => {
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| 91 | const res = tf.multinomial([-100, 1], NUM_SAMPLES, SEED);
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| 92 | expect(res.dtype).toBe('int32');
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| 93 | expect(res.shape).toEqual([NUM_SAMPLES]);
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| 94 | const outcomeProbs = computeProbs(await res.data(), 2);
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| 95 | expectArraysClose(outcomeProbs, [0, 1], EPSILON);
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| 96 | });
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| 97 | function computeProbs(events, numOutcomes) {
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| 98 | const counts = [];
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| 99 | for (let i = 0; i < numOutcomes; ++i) {
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| 100 | counts[i] = 0;
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| 101 | }
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| 102 | const numSamples = events.length;
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| 103 | for (let i = 0; i < events.length; ++i) {
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| 104 | counts[events[i]]++;
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| 105 | }
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| 106 | // Normalize counts to be probabilities between [0, 1].
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| 107 | for (let i = 0; i < counts.length; i++) {
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| 108 | counts[i] /= numSamples;
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| 109 | }
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| 110 | return counts;
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| 111 | }
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| 112 | });
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| 113 | //# 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* @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {Tensor1D} from '../tensor';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('multinomial', ALL_ENVS, () => {\n  const NUM_SAMPLES = 1000;\n  // Allowed Variance in probability (in %).\n  const EPSILON = 0.05;\n  const SEED = 3.14;\n\n  it('Flip a fair coin and check bounds', async () => {\n    const probs = tf.tensor1d([1, 1]);\n    const result = tf.multinomial(probs, NUM_SAMPLES, SEED);\n    expect(result.dtype).toBe('int32');\n    expect(result.shape).toEqual([NUM_SAMPLES]);\n    const outcomeProbs = computeProbs(await result.data(), 2);\n    expectArraysClose(outcomeProbs, [0.5, 0.5], EPSILON);\n  });\n\n  it('Flip a two-sided coin with 100% of heads', async () => {\n    const logits = tf.tensor1d([1, -100]);\n    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);\n    expect(result.dtype).toBe('int32');\n    expect(result.shape).toEqual([NUM_SAMPLES]);\n    const outcomeProbs = computeProbs(await result.data(), 2);\n    expectArraysClose(outcomeProbs, [1, 0], EPSILON);\n  });\n\n  it('Flip a two-sided coin with 100% of tails', async () => {\n    const logits = tf.tensor1d([-100, 1]);\n    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);\n    expect(result.dtype).toBe('int32');\n    expect(result.shape).toEqual([NUM_SAMPLES]);\n    const outcomeProbs = computeProbs(await result.data(), 2);\n    expectArraysClose(outcomeProbs, [0, 1], EPSILON);\n  });\n\n  it('Flip a single-sided coin throws error', () => {\n    const probs = tf.tensor1d([1]);\n    expect(() => tf.multinomial(probs, NUM_SAMPLES, SEED)).toThrowError();\n  });\n\n  it('Flip a ten-sided coin and check bounds', async () => {\n    const numOutcomes = 10;\n    const logits = tf.fill([numOutcomes], 1).as1D();\n    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);\n    expect(result.dtype).toBe('int32');\n    expect(result.shape).toEqual([NUM_SAMPLES]);\n    const outcomeProbs = computeProbs(await result.data(), numOutcomes);\n    expect(outcomeProbs.length).toBeLessThanOrEqual(numOutcomes);\n  });\n\n  it('Flip 3 three-sided coins, each coin is 100% biases', async () => {\n    const numOutcomes = 3;\n    const logits = tf.tensor2d(\n        [[-100, -100, 1], [-100, 1, -100], [1, -100, -100]], [3, numOutcomes]);\n    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);\n    expect(result.dtype).toBe('int32');\n    expect(result.shape).toEqual([3, NUM_SAMPLES]);\n\n    // First coin always gets last event.\n    let outcomeProbs =\n        computeProbs((await result.data()).slice(0, NUM_SAMPLES), numOutcomes);\n    expectArraysClose(outcomeProbs, [0, 0, 1], EPSILON);\n\n    // Second coin always gets middle event.\n    outcomeProbs = computeProbs(\n        (await result.data()).slice(NUM_SAMPLES, 2 * NUM_SAMPLES), numOutcomes);\n    expectArraysClose(outcomeProbs, [0, 1, 0], EPSILON);\n\n    // Third coin always gets first event\n    outcomeProbs =\n        computeProbs((await result.data()).slice(2 * NUM_SAMPLES), numOutcomes);\n    expectArraysClose(outcomeProbs, [1, 0, 0], EPSILON);\n  });\n\n  it('passing Tensor3D throws error', () => {\n    const probs = tf.zeros([3, 2, 2]);\n    const normalized = true;\n    expect(() => tf.multinomial(probs as Tensor1D, 3, SEED, normalized))\n        .toThrowError();\n  });\n\n  it('throws when passed a non-tensor', () => {\n    // tslint:disable-next-line:no-any\n    expect(() => tf.multinomial({} as any, NUM_SAMPLES, SEED))\n        .toThrowError(\n            /Argument 'logits' passed to 'multinomial' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object for logits (biased coin)', async () => {\n    const res = tf.multinomial([-100, 1], NUM_SAMPLES, SEED);\n    expect(res.dtype).toBe('int32');\n    expect(res.shape).toEqual([NUM_SAMPLES]);\n    const outcomeProbs = computeProbs(await res.data(), 2);\n    expectArraysClose(outcomeProbs, [0, 1], EPSILON);\n  });\n\n  function computeProbs(\n      events: Float32Array|Uint8Array|Int32Array, numOutcomes: number) {\n    const counts = [];\n    for (let i = 0; i < numOutcomes; ++i) {\n      counts[i] = 0;\n    }\n    const numSamples = events.length;\n    for (let i = 0; i < events.length; ++i) {\n      counts[events[i]]++;\n    }\n    // Normalize counts to be probabilities between [0, 1].\n    for (let i = 0; i < counts.length; i++) {\n      counts[i] /= numSamples;\n    }\n    return counts;\n  }\n});\n"]} |
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