ds-algo-study/DS-n-Algos/Dynamic-Programming/dynamic-time-warping/doc/dtw-w-comments.md

Just experimenting with publishing a package

# DTW.js

```js
var debug = require( 'debug' )( 'dtw' );
var validate = require( './validate' );
var matrix = require( './matrix' );
var comparison = require( './comparison' );

function validateOptions( options ) {
  if ( typeof options !== 'object' ) {
    throw new TypeError( 'Invalid options type: expected an object' );
  } else if ( typeof options.distanceMetric !== 'string' && typeof options.distanceFunction !== 'function' ) {
    throw new TypeError( 'Invalid distance types: expected a string distance type or a distance function' );
  } else if ( typeof options.distanceMetric === 'string' && typeof options.distanceFunction === 'function' ) {
    throw new Error( 'Invalid parameters: provide either a distance metric or function but not both' );
  }

  if ( typeof options.distanceMetric === 'string' ) {
    var normalizedDistanceMetric = options.distanceMetric.toLowerCase();
    if ( normalizedDistanceMetric !== 'manhattan' && normalizedDistanceMetric !== 'euclidean' &&
      normalizedDistanceMetric !== 'squaredeuclidean' ) {
      throw new Error( 'Invalid parameter value: Unknown distance metric \'' + options.distanceMetric + '\'' );
    }
  }
}

function retrieveDistanceFunction( distanceMetric ) {
  var normalizedDistanceMetric = distanceMetric.toLowerCase();
  var distanceFunction = null;
  if ( normalizedDistanceMetric === 'manhattan' ) {
    distanceFunction = require( './distanceFunctions/manhattan' ).distance;
  } else if ( normalizedDistanceMetric === 'euclidean' ) {
    distanceFunction = require( './distanceFunctions/euclidean' ).distance;
  } else if ( normalizedDistanceMetric === 'squaredeuclidean' ) {
    distanceFunction = require( './distanceFunctions/squaredEuclidean' ).distance;
  }

  return distanceFunction;
}

/**
 * Create a DTWOptions object
 * @class DTWOptions
 * @member {string} distanceMetric The distance metric to use: `'manhattan' | 'euclidean' | 'squaredEuclidean'`.
 * @member {function} distanceFunction The distance function to use. The function should accept two numeric arguments and return the numeric distance. e.g. function (a, b) { return a + b; }
 */

/**
 * Create a DTW object
 * @class DTW
 */
/**
 * Initializes a new instance of the `DTW`. If no options are provided the squared euclidean distance function is used.
 * @function DTW
 * @param {DTWOptions} [options] The options to initialize the dynamic time warping instance with.
 */
/**
 * Computes the optimal match between two provided sequences.
 * @method compute
 * @param {number[]} firstSequence The first sequence.
 * @param {number[]} secondSequence The second sequence.
 * @param {number} [window] The window parameter (for the locality constraint) to use.
 * @returns {number} The similarity between the provided temporal sequences.
 */
/**
 * Retrieves the optimal match between two provided sequences.
 * @method path
 * @returns {number[]} The array containing the optimal path points.
 */
var DTW = function ( options ) {
    var state = {
      distanceCostMatrix: null
    };
    if ( typeof options === 'undefined' ) {
      state.distance = require( './distanceFunctions/squaredEuclidean' ).distance;
  } else {
    validateOptions( options );
    if ( typeof options.distanceMetric === 'string' ) {
      state.distance = retrieveDistanceFunction( options.distanceMetric );
    } else if ( typeof options.distanceFunction === 'function' ) {
      state.distance = options.distanceFunction;
    }
  }

  this.compute = function ( firstSequence, secondSequence, window ) {
    var cost = Number.POSITIVE_INFINITY;
    if ( typeof window === 'undefined' ) {
      cost = computeOptimalPath( firstSequence, secondSequence, state );
    } else if ( typeof window === 'number' ) {
      cost = computeOptimalPathWithWindow( firstSequence, secondSequence, window, state );
    } else {
      throw new TypeError( 'Invalid window parameter type: expected a number' );
    }

    return cost;
  };

  this.path = function () {
    var path = null;
    if ( state.distanceCostMatrix instanceof Array ) {
      path = retrieveOptimalPath( state );
    }

    return path;
  };
};

function validateComputeParameters( s, t ) {
  validate.sequence( s, 'firstSequence' );
  validate.sequence( t, 'secondSequence' );
}

function computeOptimalPath( s, t, state ) {
  debug( '> computeOptimalPath' );
  validateComputeParameters( s, t );
  var start = new Date().getTime();
  state.m = s.length;
  state.n = t.length;
  var distanceCostMatrix = matrix.create( state.m, state.n, Number.POSITIVE_INFINITY );

  distanceCostMatrix[ 0 ][ 0 ] = state.distance( s[ 0 ], t[ 0 ] );

  for ( var rowIndex = 1; rowIndex < state.m; rowIndex++ ) {
    var cost = state.distance( s[ rowIndex ], t[ 0 ] );
    distanceCostMatrix[ rowIndex ][ 0 ] = cost + distanceCostMatrix[ rowIndex - 1 ][ 0 ];
  }

  for ( var columnIndex = 1; columnIndex < state.n; columnIndex++ ) {
    var cost = state.distance( s[ 0 ], t[ columnIndex ] );
    distanceCostMatrix[ 0 ][ columnIndex ] = cost + distanceCostMatrix[ 0 ][ columnIndex - 1 ];
  }

  for ( var rowIndex = 1; rowIndex < state.m; rowIndex++ ) {
    for ( var columnIndex = 1; columnIndex < state.n; columnIndex++ ) {
      var cost = state.distance( s[ rowIndex ], t[ columnIndex ] );
      distanceCostMatrix[ rowIndex ][ columnIndex ] =
        cost + Math.min(
          distanceCostMatrix[ rowIndex - 1 ][ columnIndex ], // Insertion
            distanceCostMatrix[ rowIndex ][ columnIndex - 1 ], // Deletion
            distanceCostMatrix[ rowIndex - 1 ][ columnIndex - 1 ] ); // Match
    }
  }

  var end = new Date().getTime();
  var time = end - start;
  debug( '< computeOptimalPath (' + time + ' ms)' );
  state.distanceCostMatrix = distanceCostMatrix;
  state.similarity = distanceCostMatrix[ state.m - 1 ][ state.n - 1 ];
  return state.similarity;
}

function computeOptimalPathWithWindow( s, t, w, state ) {
  debug( '> computeOptimalPathWithWindow' );
  validateComputeParameters( s, t );
  var start = new Date().getTime();
  state.m = s.length;
  state.n = t.length;
  var window = Math.max( w, Math.abs( s.length - t.length ) );
  var distanceCostMatrix = matrix.create( state.m + 1, state.n + 1, Number.POSITIVE_INFINITY );
  distanceCostMatrix[ 0 ][ 0 ] = 0;

  for ( var rowIndex = 1; rowIndex <= state.m; rowIndex++ ) {
    for ( var columnIndex = Math.max( 1, rowIndex - window ); columnIndex <= Math.min( state.n, rowIndex + window ); columnIndex++ ) {
      var cost = state.distance( s[ rowIndex - 1 ], t[ columnIndex - 1 ] );
      distanceCostMatrix[ rowIndex ][ columnIndex ] =
        cost + Math.min(
          distanceCostMatrix[ rowIndex - 1 ][ columnIndex ], // Insertion
            distanceCostMatrix[ rowIndex ][ columnIndex - 1 ], // Deletion
            distanceCostMatrix[ rowIndex - 1 ][ columnIndex - 1 ] ); // Match
    }
  }

  var end = new Date().getTime();
  var time = end - start;
  debug( '< computeOptimalPathWithWindow (' + time + ' ms)' );
  distanceCostMatrix.shift();
  distanceCostMatrix = distanceCostMatrix.map( function ( row ) {
    return row.slice( 1, row.length );
  } );
  state.distanceCostMatrix = distanceCostMatrix;
  state.similarity = distanceCostMatrix[ state.m - 1 ][ state.n - 1 ];
  return state.similarity;
}

function retrieveOptimalPath( state ) {
  debug( '> retrieveOptimalPath' );
  var start = new Date().getTime();

  var rowIndex = state.m - 1;
  var columnIndex = state.n - 1;
  var path = [
    [ rowIndex, columnIndex ]
  ];
  var epsilon = 1e-14;
  while ( ( rowIndex > 0 ) || ( columnIndex > 0 ) ) {
    if ( ( rowIndex > 0 ) && ( columnIndex > 0 ) ) {
      var min = Math.min(
        state.distanceCostMatrix[ rowIndex - 1 ][ columnIndex ], // Insertion
          state.distanceCostMatrix[ rowIndex ][ columnIndex - 1 ], // Deletion
          state.distanceCostMatrix[ rowIndex - 1 ][ columnIndex - 1 ] ); // Match
        if ( comparison.nearlyEqual( min, state.distanceCostMatrix[ rowIndex - 1 ][ columnIndex - 1 ], epsilon ) ) {
        rowIndex--;
        columnIndex--;
      } else if ( comparison.nearlyEqual( min, state.distanceCostMatrix[ rowIndex - 1 ][ columnIndex ], epsilon ) ) {
        rowIndex--;
      } else if ( comparison.nearlyEqual( min, state.distanceCostMatrix[ rowIndex ][ columnIndex - 1 ], epsilon ) ) {
        columnIndex--;
      }
    } else if ( ( rowIndex > 0 ) && ( columnIndex === 0 ) ) {
      rowIndex--;
    } else if ( ( rowIndex === 0 ) && ( columnIndex > 0 ) ) {
      columnIndex--;
    }

    path.push( [ rowIndex, columnIndex ] );
  }

  var end = new Date().getTime();
  var time = end - start;
  debug( '< retrieveOptimalPath (' + time + ' ms)' );
  return path.reverse();
}

module.exports = DTW;


```