41 lines
1.6 KiB
Scala
41 lines
1.6 KiB
Scala
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.scalnet.utils
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import org.nd4j.linalg.dataset.DataSet
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import org.nd4j.linalg.factory.Nd4j
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/**
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* A sequence generator that output toy examples for sequence classification
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* Features are ramdom values between 0 and 1 and class is based on whether
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* cumulative sum crossed 'timesteps * threshold'.
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* ie. for 10 timesteps and 0.25 threshold: [0.6 0.2 0.9 0.9 0.3 0.6 0.8 0.1 0.8 0.2] [0 0 0 1 1 1 1 1 1 1]
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*/
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object SequenceGenerator {
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def generate(timesteps: Int, threshold: Double = 0.25): DataSet = {
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val x = Nd4j.rand(1, timesteps)
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val y = Nd4j.create(1, timesteps)
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for (i <- 0 until timesteps) {
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val cumulativeSum = Nd4j.cumsum(x.getRow(0), 1)
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val limit = timesteps * threshold
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y.putScalar(0, i, if (cumulativeSum.getDouble(0l, i) > limit) 1 else 0)
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}
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new DataSet(x.reshape(1, timesteps, 1), y.reshape(1, timesteps, 1))
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}
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}
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