src/nupic/swarming/hypersearch/permutation_helpers.py
def pushAwayFrom(self, otherPositions, rng):
    """See comments in base class."""
    # If min and max are the same, nothing to do
    if self.max == self.min:
      return

    # How many potential other positions to evaluate?
    numPositions = len(otherPositions) * 4
    if numPositions == 0:
      return

    # Assign a weight to each potential position based on how close it is
    # to other particles.
    stepSize = float(self.max-self.min) / numPositions
    positions = numpy.arange(self.min, self.max + stepSize, stepSize)

    # Get rid of duplicates.
    numPositions = len(positions)
    weights = numpy.zeros(numPositions)

    # Assign a weight to each potential position, based on a gaussian falloff
    # from each existing variable. The weight of a variable to each potential
    # position is given as:
    #    e ^ -(dist^2/stepSize^2)
    maxDistanceSq = -1 * (stepSize ** 2)
    for pos in otherPositions:
      distances = pos - positions
      varWeights = numpy.exp(numpy.power(distances, 2) / maxDistanceSq)
      weights += varWeights


    # Put this particle at the position with smallest weight.
    positionIdx = weights.argmin()
    self._position = positions[positionIdx]

    # Set its best position to this.
    self._bestPosition = self.getPosition()

    # Give it a random direction.
    self._velocity *= rng.choice([1, -1])
def pushAwayFrom(self, otherPositions, rng):
    """See comments in base class."""
    # If min and max are the same, nothing to do
    if self.max == self.min:
      return

    # How many potential other positions to evaluate?
    numPositions = len(otherPositions) * 4
    if numPositions == 0:
      return

    # Assign a weight to each potential position based on how close it is
    # to other particles.
    stepSize = float(self.max-self.min) / numPositions
    positions = numpy.arange(self.min, self.max + stepSize, stepSize)

    # Get rid of duplicates.
    numPositions = len(positions)
    weights = numpy.zeros(numPositions)

    # Assign a weight to each potential position, based on a gaussian falloff
    # from each existing variable. The weight of a variable to each potential
    # position is given as:
    #    e ^ -(dist^2/stepSize^2)
    maxDistanceSq = -1 * (stepSize ** 2)
    for pos in otherPositions:
      distances = pos - positions
      varWeights = numpy.exp(numpy.power(distances, 2) / maxDistanceSq)
      weights += varWeights


    # Put this particle at the position with smallest weight.
    positionIdx = weights.argmin()
    self._position = positions[positionIdx]

    # Set its best position to this.
    self._bestPosition = self.getPosition()

    # Give it a random direction.
    self._velocity *= rng.choice([1, -1])
def pushAwayFrom ( self , otherPositions , rng ) : # If min and max are the same, nothing to do if self . max == self . min : return # How many potential other positions to evaluate? numPositions = len ( otherPositions ) * 4 if numPositions == 0 : return # Assign a weight to each potential position based on how close it is # to other particles. stepSize = float ( self . max - self . min ) / numPositions positions = numpy . arange ( self . min , self . max + stepSize , stepSize ) # Get rid of duplicates. numPositions = len ( positions ) weights = numpy . zeros ( numPositions ) # Assign a weight to each potential position, based on a gaussian falloff # from each existing variable. The weight of a variable to each potential # position is given as: #    e ^ -(dist^2/stepSize^2) maxDistanceSq = - 1 * ( stepSize ** 2 ) for pos in otherPositions : distances = pos - positions varWeights = numpy . exp ( numpy . power ( distances , 2 ) / maxDistanceSq ) weights += varWeights # Put this particle at the position with smallest weight. positionIdx = weights . argmin ( ) self . _position = positions [ positionIdx ] # Set its best position to this. self . _bestPosition = self . getPosition ( ) # Give it a random direction. self . _velocity *= rng . choice ( [ 1 , - 1 ] )
See comments in base class.
See comments in base class .