DftNormalization.java
- /*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You 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
- *
- * https://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.
- */
- /*
- * This is not the original file distributed by the Apache Software Foundation
- * It has been modified by the Hipparchus project
- */
- package org.hipparchus.transform;
- /**
- * This enumeration defines the various types of normalizations that can be
- * applied to discrete Fourier transforms (DFT). The exact definition of these
- * normalizations is detailed below.
- *
- * @see FastFourierTransformer
- */
- public enum DftNormalization {
- /**
- * Should be passed to the constructor of {@link FastFourierTransformer}
- * to use the <em>standard</em> normalization convention. This normalization
- * convention is defined as follows
- * <ul>
- * <li>forward transform: y<sub>n</sub> = ∑<sub>k=0</sub><sup>N-1</sup>
- * x<sub>k</sub> exp(-2πi n k / N),</li>
- * <li>inverse transform: x<sub>k</sub> = N<sup>-1</sup>
- * ∑<sub>n=0</sub><sup>N-1</sup> y<sub>n</sub> exp(2πi n k / N),</li>
- * </ul>
- * where N is the size of the data sample.
- */
- STANDARD,
- /**
- * Should be passed to the constructor of {@link FastFourierTransformer}
- * to use the <em>unitary</em> normalization convention. This normalization
- * convention is defined as follows
- * <ul>
- * <li>forward transform: y<sub>n</sub> = (1 / √N)
- * ∑<sub>k=0</sub><sup>N-1</sup> x<sub>k</sub>
- * exp(-2πi n k / N),</li>
- * <li>inverse transform: x<sub>k</sub> = (1 / √N)
- * ∑<sub>n=0</sub><sup>N-1</sup> y<sub>n</sub> exp(2πi n k / N),</li>
- * </ul>
- * which makes the transform unitary. N is the size of the data sample.
- */
- UNITARY
- }