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2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2020 Google Inc. 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('erf', ALL_ENVS, () => {
    it('basic', async () => {
        const values = [-0.25, 0.25, 0.5, .75, -0.4];
        const a = tf.tensor1d(values);
        const result = tf.erf(a);
        const expected = [-0.2763264, 0.2763264, 0.5204999, 0.7111556, -0.4283924];
        expectArraysClose(await result.data(), expected);
    });
    it('blowup', async () => {
        const values = [-1.4, -2.5, -3.1, -4.4];
        const a = tf.tensor1d(values);
        const result = tf.erf(a);
        const expected = [-0.9522852, -0.999593, -0.9999883, -1];
        expectArraysClose(await result.data(), expected);
    });
    it('scalar', async () => {
        const a = tf.scalar(1);
        const result = tf.erf(a);
        const expected = [0.8427008];
        expectArraysClose(await result.data(), expected);
    });
    it('scalar in int32', async () => {
        const a = tf.scalar(1, 'int32');
        const result = tf.erf(a);
        const expected = [0.8427008];
        expectArraysClose(await result.data(), expected);
    });
    it('tensor2d', async () => {
        const values = [0.2, 0.3, 0.4, 0.5];
        const a = tf.tensor2d(values, [2, 2]);
        const result = tf.erf(a);
        const expected = [0.2227026, 0.32862678, 0.42839235, 0.5204999];
        expectArraysClose(await result.data(), expected);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor1d([0.5, NaN, 0]);
        const res = tf.erf(a);
        expectArraysClose(await res.data(), [0.5204999, NaN, 0.0]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(0.5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.erf(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * 2 * Math.exp(-0.5 * 0.5) / Math.sqrt(Math.PI)]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(0.5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.erf(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * 2 * Math.exp(-0.5 * 0.5) / Math.sqrt(Math.PI)]);
    });
    it('gradients: Tensor1D', async () => {
        const aValues = [-0.1, 0.2, 0.3, -0.5];
        const dyValues = [1, 2, 3, 4];
        const a = tf.tensor1d(aValues);
        const dy = tf.tensor1d(dyValues);
        const gradients = tf.grad(a => tf.erf(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /
                Math.sqrt(Math.PI);
        }
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), expected);
    });
    it('gradients: Tensor2D', async () => {
        const aValues = [-0.3, 0.1, 0.2, 0.3];
        const dyValues = [1, 2, 3, 4];
        const a = tf.tensor2d(aValues, [2, 2]);
        const dy = tf.tensor2d(dyValues, [2, 2]);
        const gradients = tf.grad(a => tf.erf(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /
                Math.sqrt(Math.PI);
        }
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), expected);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.erf({}))
            .toThrowError(/Argument 'x' passed to 'erf' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const result = tf.erf(1);
        expectArraysClose(await result.data(), [0.8427008]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.erf('q'))
            .toThrowError(/Argument 'x' passed to 'erf' must be numeric/);
    });
});
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* @license\n * Copyright 2020 Google Inc. 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 {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('erf', ALL_ENVS, () => {\n  it('basic', async () => {\n    const values = [-0.25, 0.25, 0.5, .75, -0.4];\n    const a = tf.tensor1d(values);\n    const result = tf.erf(a);\n    const expected = [-0.2763264, 0.2763264, 0.5204999, 0.7111556, -0.4283924];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('blowup', async () => {\n    const values = [-1.4, -2.5, -3.1, -4.4];\n    const a = tf.tensor1d(values);\n    const result = tf.erf(a);\n    const expected = [-0.9522852, -0.999593, -0.9999883, -1];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('scalar', async () => {\n    const a = tf.scalar(1);\n    const result = tf.erf(a);\n    const expected = [0.8427008];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('scalar in int32', async () => {\n    const a = tf.scalar(1, 'int32');\n    const result = tf.erf(a);\n    const expected = [0.8427008];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('tensor2d', async () => {\n    const values = [0.2, 0.3, 0.4, 0.5];\n    const a = tf.tensor2d(values, [2, 2]);\n    const result = tf.erf(a);\n    const expected = [0.2227026, 0.32862678, 0.42839235, 0.5204999];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('propagates NaNs', async () => {\n    const a = tf.tensor1d([0.5, NaN, 0]);\n    const res = tf.erf(a);\n    expectArraysClose(await res.data(), [0.5204999, NaN, 0.0]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(0.5);\n    const dy = tf.scalar(8);\n    const gradients = tf.grad(a => tf.erf(a))(a, dy);\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(),\n        [8 * 2 * Math.exp(-0.5 * 0.5) / Math.sqrt(Math.PI)]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.scalar(0.5);\n    const dy = tf.scalar(8);\n    const gradients = tf.grad(a => tf.erf(a.clone()).clone())(a, dy);\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(),\n        [8 * 2 * Math.exp(-0.5 * 0.5) / Math.sqrt(Math.PI)]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const aValues = [-0.1, 0.2, 0.3, -0.5];\n    const dyValues = [1, 2, 3, 4];\n    const a = tf.tensor1d(aValues);\n    const dy = tf.tensor1d(dyValues);\n    const gradients = tf.grad(a => tf.erf(a))(a, dy);\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /\n          Math.sqrt(Math.PI);\n    }\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), expected);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const aValues = [-0.3, 0.1, 0.2, 0.3];\n    const dyValues = [1, 2, 3, 4];\n    const a = tf.tensor2d(aValues, [2, 2]);\n    const dy = tf.tensor2d(dyValues, [2, 2]);\n    const gradients = tf.grad(a => tf.erf(a))(a, dy);\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /\n          Math.sqrt(Math.PI);\n    }\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), expected);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.erf({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'erf' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const result = tf.erf(1);\n    expectArraysClose(await result.data(), [0.8427008]);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.erf('q'))\n        .toThrowError(/Argument 'x' passed to 'erf' must be numeric/);\n  });\n});\n"]}