data.js

/**
 * @license
 * Copyright 2018 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 '@tensorflow/tfjs';

export function generateData(numPoints, coeff, sigma = 0.04) {
  return tf.tidy(() => {
    const [a, b, c, d] = [
      tf.scalar(coeff.a), tf.scalar(coeff.b), tf.scalar(coeff.c),
      tf.scalar(coeff.d)
    ];

    const xs = tf.randomUniform([numPoints], -1, 1);

    // Generate polynomial data
    const three = tf.scalar(3, 'int32');
    const ys = a.mul(xs.pow(three))
      .add(b.mul(xs.square()))
      .add(c.mul(xs))
      .add(d)
      // Add random noise to the generated data
      // to make the problem a bit more interesting
      .add(tf.randomNormal([numPoints], 0, sigma));

    // Normalize the y values to the range 0 to 1.
    const ymin = ys.min();
    const ymax = ys.max();
    const yrange = ymax.sub(ymin);
    const ysNormalized = ys.sub(ymin).div(yrange);

    return {
      xs, 
      ys: ysNormalized
    };
  })
}