/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
describeWithFlags('multinomial', ALL_ENVS, () => {
    const NUM_SAMPLES = 1000;
    // Allowed Variance in probability (in %).
    const EPSILON = 0.05;
    const SEED = 3.14;
    it('Flip a fair coin and check bounds', async () => {
        const probs = tf.tensor1d([1, 1]);
        const result = tf.multinomial(probs, NUM_SAMPLES, SEED);
        expect(result.dtype).toBe('int32');
        expect(result.shape).toEqual([NUM_SAMPLES]);
        const outcomeProbs = computeProbs(await result.data(), 2);
        expectArraysClose(outcomeProbs, [0.5, 0.5], EPSILON);
    });
    it('Flip a two-sided coin with 100% of heads', async () => {
        const logits = tf.tensor1d([1, -100]);
        const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
        expect(result.dtype).toBe('int32');
        expect(result.shape).toEqual([NUM_SAMPLES]);
        const outcomeProbs = computeProbs(await result.data(), 2);
        expectArraysClose(outcomeProbs, [1, 0], EPSILON);
    });
    it('Flip a two-sided coin with 100% of tails', async () => {
        const logits = tf.tensor1d([-100, 1]);
        const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
        expect(result.dtype).toBe('int32');
        expect(result.shape).toEqual([NUM_SAMPLES]);
        const outcomeProbs = computeProbs(await result.data(), 2);
        expectArraysClose(outcomeProbs, [0, 1], EPSILON);
    });
    it('Flip a single-sided coin throws error', () => {
        const probs = tf.tensor1d([1]);
        expect(() => tf.multinomial(probs, NUM_SAMPLES, SEED)).toThrowError();
    });
    it('Flip a ten-sided coin and check bounds', async () => {
        const numOutcomes = 10;
        const logits = tf.fill([numOutcomes], 1).as1D();
        const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
        expect(result.dtype).toBe('int32');
        expect(result.shape).toEqual([NUM_SAMPLES]);
        const outcomeProbs = computeProbs(await result.data(), numOutcomes);
        expect(outcomeProbs.length).toBeLessThanOrEqual(numOutcomes);
    });
    it('Flip 3 three-sided coins, each coin is 100% biases', async () => {
        const numOutcomes = 3;
        const logits = tf.tensor2d([[-100, -100, 1], [-100, 1, -100], [1, -100, -100]], [3, numOutcomes]);
        const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
        expect(result.dtype).toBe('int32');
        expect(result.shape).toEqual([3, NUM_SAMPLES]);
        // First coin always gets last event.
        let outcomeProbs = computeProbs((await result.data()).slice(0, NUM_SAMPLES), numOutcomes);
        expectArraysClose(outcomeProbs, [0, 0, 1], EPSILON);
        // Second coin always gets middle event.
        outcomeProbs = computeProbs((await result.data()).slice(NUM_SAMPLES, 2 * NUM_SAMPLES), numOutcomes);
        expectArraysClose(outcomeProbs, [0, 1, 0], EPSILON);
        // Third coin always gets first event
        outcomeProbs =
            computeProbs((await result.data()).slice(2 * NUM_SAMPLES), numOutcomes);
        expectArraysClose(outcomeProbs, [1, 0, 0], EPSILON);
    });
    it('passing Tensor3D throws error', () => {
        const probs = tf.zeros([3, 2, 2]);
        const normalized = true;
        expect(() => tf.multinomial(probs, 3, SEED, normalized))
            .toThrowError();
    });
    it('throws when passed a non-tensor', () => {
        // tslint:disable-next-line:no-any
        expect(() => tf.multinomial({}, NUM_SAMPLES, SEED))
            .toThrowError(/Argument 'logits' passed to 'multinomial' must be a Tensor/);
    });
    it('accepts a tensor-like object for logits (biased coin)', async () => {
        const res = tf.multinomial([-100, 1], NUM_SAMPLES, SEED);
        expect(res.dtype).toBe('int32');
        expect(res.shape).toEqual([NUM_SAMPLES]);
        const outcomeProbs = computeProbs(await res.data(), 2);
        expectArraysClose(outcomeProbs, [0, 1], EPSILON);
    });
    it('creates the same data given the same seed', async () => {
        const res1 = tf.multinomial([1, 2, 3, 4], NUM_SAMPLES, SEED);
        const res2 = tf.multinomial([1, 2, 3, 4], NUM_SAMPLES, SEED);
        expectArraysClose(await res1.data(), await res2.data());
    });
    function computeProbs(events, numOutcomes) {
        const counts = [];
        for (let i = 0; i < numOutcomes; ++i) {
            counts[i] = 0;
        }
        const numSamples = events.length;
        for (let i = 0; i < events.length; ++i) {
            counts[events[i]]++;
        }
        // Normalize counts to be probabilities between [0, 1].
        for (let i = 0; i < counts.length; i++) {
            counts[i] /= numSamples;
        }
        return counts;
    }
});
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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  it('creates the same data given the same seed', async () => {\n    const res1 = tf.multinomial([1, 2, 3, 4], NUM_SAMPLES, SEED);\n    const res2 = tf.multinomial([1, 2, 3, 4], NUM_SAMPLES, SEED);\n    expectArraysClose(await res1.data(), await res2.data());\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"]}