| 1 | {"version":3,"file":"random_normal.js","sourceRoot":"","sources":["../../src/ops/random_normal.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAKH,OAAO,EAAC,MAAM,EAAC,MAAM,UAAU,CAAC;AAChC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,WAAW,EAAC,MAAM,aAAa,CAAC;AAExC;;;;;;;;;;;;;;GAcG;AACH,SAAS,aAAa,CAClB,KAAkB,EAAE,IAAI,GAAG,CAAC,EAAE,MAAM,GAAG,CAAC,EAAE,KAAyB,EACnE,IAAa;IACf,IAAI,KAAK,IAAI,IAAI,IAAK,KAAkB,KAAK,MAAM,EAAE;QACnD,MAAM,IAAI,KAAK,CAAC,yBAAyB,KAAK,EAAE,CAAC,CAAC;KACnD;IACD,MAAM,SAAS,GACX,IAAI,WAAW,CAAC,IAAI,EAAE,MAAM,EAAE,KAAK,EAAE,KAAK,CAAC,eAAe,EAAE,IAAI,CAAC,CAAC;IACtE,MAAM,GAAG,GAAG,MAAM,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC;IACjC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,GAAG,CAAC,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QAC1C,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,SAAS,EAAE,CAAC;KACvC;IACD,OAAO,GAAG,CAAC,QAAQ,EAAE,CAAC;AACxB,CAAC;AAED,MAAM,CAAC,MAAM,YAAY,GAAG,EAAE,CAAC,EAAC,aAAa,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 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 {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_<R extends Rank>(\n shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n seed?: number): Tensor<R> {\n if (dtype != null && (dtype as DataType) === 'bool') {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const randGauss =\n new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\n\nexport const randomNormal = op({randomNormal_});\n"]} |