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Trainer Agnostic Deep Learning

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/*
 * Copyright (c) 2016, Peter Abeles. All Rights Reserved.
 *
 * This file is part of DeepBoof
 *
 * 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.
 */

package deepboof.tensors;

import deepboof.Tensor;

import java.util.Arrays;

/**
 * @author Peter Abeles
 */
public class Tensor_U8 extends Tensor {

	public byte d[] = new byte[0];

	public Tensor_U8(int... shape ) {
		reshape(shape);
	}

	public Tensor_U8(){}

	public static Tensor_U8 wrap( byte[] data , int...shape) {
		Tensor_U8 t = new Tensor_U8();
		t.d = data;
		t.shape =shape.clone();
		t.computeStrides();
		return t;
	}

	@Override
	public double getDouble(int... coordinate) {
		return d[idx(coordinate)];
	}

	@Override
	public Object getData() {
		return d;
	}

	@Override
	public void setData(Object data) {
		this.d = (byte[])data;
	}

	@Override
	protected void innerArrayGrow(int N) {
		if( d.length < N ) {
			d = new byte[N];
		}
	}

	@Override
	protected int innerArrayLength() {
		return d.length;
	}

	@Override
	public Tensor_U8 create(int... shape) {
		return new Tensor_U8(shape);
	}

	@Override
	public void zero() {
		Arrays.fill(d,startIndex,startIndex+length(),(byte)0);
	}

	@Override
	public Class getDataType() {
		return byte.class;
	}
}