(
dataset, isTrainData = true, vectorMeans = [], vectorStddevs = [])
| 135 | * standard deviation of each vector column. |
| 136 | */ |
| 137 | export function normalizeDataset( |
| 138 | dataset, isTrainData = true, vectorMeans = [], vectorStddevs = []) { |
| 139 | const numFeatures = dataset[0].length; |
| 140 | let vectorMean; |
| 141 | let vectorStddev; |
| 142 | |
| 143 | for (let i = 0; i < numFeatures; i++) { |
| 144 | const vector = dataset.map(row => row[i]); |
| 145 | |
| 146 | if (isTrainData) { |
| 147 | vectorMean = mean(vector); |
| 148 | vectorStddev = stddev(vector); |
| 149 | |
| 150 | vectorMeans.push(vectorMean); |
| 151 | vectorStddevs.push(vectorStddev); |
| 152 | } else { |
| 153 | vectorMean = vectorMeans[i]; |
| 154 | vectorStddev = vectorStddevs[i]; |
| 155 | } |
| 156 | |
| 157 | const vectorNormalized = |
| 158 | normalizeVector(vector, vectorMean, vectorStddev); |
| 159 | |
| 160 | vectorNormalized.forEach((value, index) => { |
| 161 | dataset[index][i] = value; |
| 162 | }); |
| 163 | } |
| 164 | |
| 165 | return {dataset, vectorMeans, vectorStddevs}; |
| 166 | }; |
| 167 | |
| 168 | /** |
| 169 | * Binarizes a tensor based on threshold of 0.5. |
nothing calls this directly
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