Forecasting til elise notes with brain js, rendered example from https://github.com/torgeir/brainjs/blob/master/solutions/forecasting-til-elise.js

forecasting-til-elise-notes.js

function anonymous(rawInput
) {

  var input = rawInput;
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  var output = [];
  var states = [];
  var prevStates;
  var state;
  var max = input.length;
  for (var _i = 0; _i < max; _i++) {
    prevStates = states;
    states = [];
    states[0] = {
      name: 'forwardFn',
      left: null,
      right: null,
      product: new Matrix(1, 1)
    };
    states[1] = {
      name: 'multiply',
      left: json.hiddenLayers[0].inputMatrix,
      right: states[0].product,
      product: new Matrix(10, 1)
    };
    states[2] = {
      name: 'multiply',
      left: json.hiddenLayers[0].inputHidden,
      right: typeof prevStates[25] === 'object' ? prevStates[25].product : new Matrix(10, 1),
      product: new Matrix(10, 1)
    };
    states[3] = {
      name: 'add',
      left: states[1].product,
      right: states[2].product,
      product: new Matrix(10, 1)
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    states[4] = {
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      left: states[3].product,
      right: json.hiddenLayers[0].inputBias,
      product: new Matrix(10, 1)
    };
    states[5] = {
      name: 'sigmoid',
      left: states[4].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[6] = {
      name: 'multiply',
      left: json.hiddenLayers[0].forgetMatrix,
      right: states[0].product,
      product: new Matrix(10, 1)
    };
    states[7] = {
      name: 'multiply',
      left: json.hiddenLayers[0].forgetHidden,
      right: states[2].right,
      product: new Matrix(10, 1)
    };
    states[8] = {
      name: 'add',
      left: states[6].product,
      right: states[7].product,
      product: new Matrix(10, 1)
    };
    states[9] = {
      name: 'add',
      left: states[8].product,
      right: json.hiddenLayers[0].forgetBias,
      product: new Matrix(10, 1)
    };
    states[10] = {
      name: 'sigmoid',
      left: states[9].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[11] = {
      name: 'multiply',
      left: json.hiddenLayers[0].outputMatrix,
      right: states[0].product,
      product: new Matrix(10, 1)
    };
    states[12] = {
      name: 'multiply',
      left: json.hiddenLayers[0].outputHidden,
      right: states[2].right,
      product: new Matrix(10, 1)
    };
    states[13] = {
      name: 'add',
      left: states[11].product,
      right: states[12].product,
      product: new Matrix(10, 1)
    };
    states[14] = {
      name: 'add',
      left: states[13].product,
      right: json.hiddenLayers[0].outputBias,
      product: new Matrix(10, 1)
    };
    states[15] = {
      name: 'sigmoid',
      left: states[14].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[16] = {
      name: 'multiply',
      left: json.hiddenLayers[0].cellActivationMatrix,
      right: states[0].product,
      product: new Matrix(10, 1)
    };
    states[17] = {
      name: 'multiply',
      left: json.hiddenLayers[0].cellActivationHidden,
      right: states[2].right,
      product: new Matrix(10, 1)
    };
    states[18] = {
      name: 'add',
      left: states[16].product,
      right: states[17].product,
      product: new Matrix(10, 1)
    };
    states[19] = {
      name: 'add',
      left: states[18].product,
      right: json.hiddenLayers[0].cellActivationBias,
      product: new Matrix(10, 1)
    };
    states[20] = {
      name: 'tanh',
      left: states[19].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[21] = {
      name: 'multiplyElement',
      left: states[10].product,
      right: states[2].right,
      product: new Matrix(10, 1)
    };
    states[22] = {
      name: 'multiplyElement',
      left: states[5].product,
      right: states[20].product,
      product: new Matrix(10, 1)
    };
    states[23] = {
      name: 'add',
      left: states[21].product,
      right: states[22].product,
      product: new Matrix(10, 1)
    };
    states[24] = {
      name: 'tanh',
      left: states[23].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[25] = {
      name: 'multiplyElement',
      left: states[15].product,
      right: states[24].product,
      product: new Matrix(10, 1)
    };
    states[26] = {
      name: 'multiply',
      left: json.hiddenLayers[1].inputMatrix,
      right: states[25].product,
      product: new Matrix(10, 1)
    };
    states[27] = {
      name: 'multiply',
      left: json.hiddenLayers[1].inputHidden,
      right: typeof prevStates[50] === 'object' ? prevStates[50].product : new Matrix(10, 1),
      product: new Matrix(10, 1)
    };
    states[28] = {
      name: 'add',
      left: states[26].product,
      right: states[27].product,
      product: new Matrix(10, 1)
    };
    states[29] = {
      name: 'add',
      left: states[28].product,
      right: json.hiddenLayers[1].inputBias,
      product: new Matrix(10, 1)
    };
    states[30] = {
      name: 'sigmoid',
      left: states[29].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[31] = {
      name: 'multiply',
      left: json.hiddenLayers[1].forgetMatrix,
      right: states[25].product,
      product: new Matrix(10, 1)
    };
    states[32] = {
      name: 'multiply',
      left: json.hiddenLayers[1].forgetHidden,
      right: states[27].right,
      product: new Matrix(10, 1)
    };
    states[33] = {
      name: 'add',
      left: states[31].product,
      right: states[32].product,
      product: new Matrix(10, 1)
    };
    states[34] = {
      name: 'add',
      left: states[33].product,
      right: json.hiddenLayers[1].forgetBias,
      product: new Matrix(10, 1)
    };
    states[35] = {
      name: 'sigmoid',
      left: states[34].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[36] = {
      name: 'multiply',
      left: json.hiddenLayers[1].outputMatrix,
      right: states[25].product,
      product: new Matrix(10, 1)
    };
    states[37] = {
      name: 'multiply',
      left: json.hiddenLayers[1].outputHidden,
      right: states[27].right,
      product: new Matrix(10, 1)
    };
    states[38] = {
      name: 'add',
      left: states[36].product,
      right: states[37].product,
      product: new Matrix(10, 1)
    };
    states[39] = {
      name: 'add',
      left: states[38].product,
      right: json.hiddenLayers[1].outputBias,
      product: new Matrix(10, 1)
    };
    states[40] = {
      name: 'sigmoid',
      left: states[39].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[41] = {
      name: 'multiply',
      left: json.hiddenLayers[1].cellActivationMatrix,
      right: states[25].product,
      product: new Matrix(10, 1)
    };
    states[42] = {
      name: 'multiply',
      left: json.hiddenLayers[1].cellActivationHidden,
      right: states[27].right,
      product: new Matrix(10, 1)
    };
    states[43] = {
      name: 'add',
      left: states[41].product,
      right: states[42].product,
      product: new Matrix(10, 1)
    };
    states[44] = {
      name: 'add',
      left: states[43].product,
      right: json.hiddenLayers[1].cellActivationBias,
      product: new Matrix(10, 1)
    };
    states[45] = {
      name: 'tanh',
      left: states[44].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[46] = {
      name: 'multiplyElement',
      left: states[35].product,
      right: states[27].right,
      product: new Matrix(10, 1)
    };
    states[47] = {
      name: 'multiplyElement',
      left: states[30].product,
      right: states[45].product,
      product: new Matrix(10, 1)
    };
    states[48] = {
      name: 'add',
      left: states[46].product,
      right: states[47].product,
      product: new Matrix(10, 1)
    };
    states[49] = {
      name: 'tanh',
      left: states[48].product,
      right: null,
      product: new Matrix(10, 1)
    };
    states[50] = {
      name: 'multiplyElement',
      left: states[40].product,
      right: states[49].product,
      product: new Matrix(10, 1)
    };
    states[51] = {
      name: 'multiply',
      left: json.outputConnector,
      right: states[50].product,
      product: new Matrix(1, 1)
    };
    states[52] = {
      name: 'add',
      left: states[51].product,
      right: json.output,
      product: new Matrix(1, 1)
    };
    for (var stateIndex = 0, stateMax = 53; stateIndex < stateMax; stateIndex++) {
      state = states[stateIndex];
      var product = state.product;
      var left = state.left;
      var right = state.right;

      switch (state.name) {
        case 'forwardFn':
          
                  product.weights = _input.weights = _this.inputValue;
                
          break;
        case 'multiply': //compiled from src/recurrent/matrix/multiply.js
          
          var leftRows = left.rows;
          var leftColumns = left.columns;
          var rightColumns = right.columns; // loop over rows of left
        
          for (var leftRow = 0; leftRow < leftRows; leftRow++) {
            var leftRowBase = leftColumns * leftRow;
            var rightRowBase = rightColumns * leftRow; // loop over cols of right
        
            for (var rightColumn = 0; rightColumn < rightColumns; rightColumn++) {
              // dot product loop
              var dot = 0; // loop over columns of left
        
              for (var leftColumn = 0; leftColumn < leftColumns; leftColumn++) {
                var rightColumnBase = rightColumns * leftColumn;
                var leftIndex = leftRowBase + leftColumn;
                var rightIndex = rightColumnBase + rightColumn;
                dot += left.weights[leftIndex] * right.weights[rightIndex];
                
                
              }
        
              product.weights[rightRowBase + rightColumn] = dot;
            }
          }
        
          break;
        case 'add': //compiled from src/recurrent/matrix/add.js
          
          for (var i = 0; i < left.weights.length; i++) {
            product.weights[i] = left.weights[i] + right.weights[i];
            
          }
        
          break;
        case 'sigmoid': //compiled from src/recurrent/matrix/sigmoid.js
          
          // sigmoid nonlinearity
          for (var i = 0; i < left.weights.length; i++) {
            product.weights[i] = 1 / (1 + Math.exp(-left.weights[i]));
            
          }
        
          break;
        case 'tanh': //compiled from src/recurrent/matrix/tanh.js
          
          // tanh nonlinearity
          for (var i = 0; i < left.weights.length; i++) {
            product.weights[i] = Math.tanh(left.weights[i]);
            
          }
        
          break;
        case 'multiplyElement': //compiled from src/recurrent/matrix/multiply-element.js
          
          var weights = left.weights;
        
          for (var i = 0; i < weights.length; i++) {
            product.weights[i] = left.weights[i] * right.weights[i];
            
          }
        
          break;
      }
    }
    output = state.product.weights;
  }
  return output[0];
  
  

  function Matrix(rows, columns) {
    this.rows = rows;
    this.columns = columns;
    this.weights = zeros(rows * columns);
  }
  function zeros(size) {
  return new Float32Array(size);
}
  function softmax(m) {
  var result = new Matrix(m.rows, m.columns); // probability volume

  var maxVal = -999999;

  for (var i = 0; i < m.weights.length; i++) {
    if (m.weights[i] > maxVal) {
      maxVal = m.weights[i];
    }
  }

  var s = 0;

  for (var _i = 0; _i < m.weights.length; _i++) {
    result.weights[_i] = Math.exp(m.weights[_i] - maxVal);
    s += result.weights[_i];
  }

  for (var _i2 = 0; _i2 < m.weights.length; _i2++) {
    result.weights[_i2] /= s;
  } // no backward pass here needed
  // since we will use the computed probabilities outside
  // to set gradients directly on m


  return result;
}
  function randomFloat(a, b) {
  return Math.random() * (b - a) + a;
}
  function sampleI(m) {
  // sample argmax from w, assuming w are
  // probabilities that sum to one
  var r = randomFloat(0, 1);
  var x = 0;
  var i = 0;
  var w = m.weights;

  while (true) {
    x += w[i];

    if (x > r) {
      return i;
    }

    i++;
  }
}
  function maxI(m) {
  // argmax of array w
  var weights = m.weights;
  var maxv = weights[0];
  var maxix = 0;

  for (var i = 1; i < weights.length; i++) {
    var v = weights[i];
    if (v < maxv) continue;
    maxix = i;
    maxv = v;
  }

  return maxix;
}
}