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Created May 18, 2020
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* Calculates the mean and standard deviation of each column of an array.
* @param {Tensor2d} data Dataset from which to calculate the mean and
* std of each column independently.
* @returns {Object} Contains the mean and std of each vector
* column as 1d tensors.
function determineMeanAndStddev_(data) {
const dataMean = data.mean(0);
const diffFromMean = data.sub(dataMean);
const squaredDiffFromMean = diffFromMean.square();
const variance = squaredDiffFromMean.mean(0);
const dataStd = variance.sqrt();
return { dataMean, dataStd };
* Given expected mean and standard deviation, normalizes a dataset by
* subtracting the mean and dividing by the standard deviation.
* @param {Tensor2d} data: Data to normalize.
* Shape: [numSamples, numFeatures].
* @param {Tensor1d} mean: Expected mean of the data. Shape [numFeatures].
* @param {Tensor1d} std: Expected std of the data. Shape [numFeatures]
* @returns {Tensor2d}: Tensor the same shape as data, but each column
* normalized to have zero mean and unit standard deviation.
function normalizeTensor_(data, dataMean, dataStd) {
return data.sub(dataMean).div(dataStd);
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