def mmGetCellActivityPlot(self,
title=None,
showReset=False,
resetShading=0.25):
""" Returns plot of the cell activity.
@param title an optional title for the figure
@param showReset if true, the first set of cell activities after a reset
will have a gray background
@param resetShading If showReset is true, this float specifies the
intensity of the reset background with 0.0 being white and 1.0 being black
@return (Plot) plot
"""
plot = Plot(self, title)
resetTrace = self.mmGetTraceResets().data
activeCellTrace = self._mmTraces["activeCells"].data
data = numpy.zeros((self._numColumns, 1))
for i in xrange(len(activeCellTrace)):
if showReset and resetTrace[i]:
activity = numpy.ones((self._numColumns, 1)) * resetShading
else:
activity = numpy.zeros((self._numColumns, 1))
activeSet = activeCellTrace[i]
activity[list(activeSet)] = 1
data = numpy.concatenate((data, activity), 1)
plot.add2DArray(data, xlabel="Time", ylabel="Cell Activity")
return plot
def mmGetPermanencesPlot(self, title=None):
""" Returns plot of column permanences.
@param title an optional title for the figure
@return (Plot) plot
"""
plot = Plot(self, title)
data = numpy.zeros((self.getNumColumns(), self.getNumInputs()))
for i in xrange(self.getNumColumns()):
self.getPermanence(i, data[i])
plot.add2DArray(data, xlabel="Permanences", ylabel="Column")
return plot
def mmGetPermanencesPlot(self, title=None):
""" Returns plot of column permanences.
@param title an optional title for the figure
@return (Plot) plot
"""
plot = Plot(self, title)
data = numpy.zeros((self.getNumColumns(), self.getNumInputs()))
for i in xrange(self.getNumColumns()):
self.getPermanence(i, data[i])
plot.add2DArray(data, xlabel="Permanences", ylabel="Column")
return plot
def mmGetPlotConnectionsPerColumn(self, title=None):
"""
Returns plot of # connections per column.
@return (Plot) plot
"""
plot = Plot(self, title)
plot.addGraph(sorted(self._connectedCounts.tolist(), reverse=True),
position=211,
xlabel="column", ylabel="# connections")
plot.addHistogram(self._connectedCounts.tolist(),
position=212,
bins=len(self._connectedCounts) / 10,
xlabel="# connections", ylabel="# columns")
return plot
def mmGetCellTracePlot(self,
cellTrace,
cellCount,
activityType,
title="",
showReset=False,
resetShading=0.25):
"""
Returns plot of the cell activity. Note that if many timesteps of
activities are input, matplotlib's image interpolation may omit activities
(columns in the image).
@param cellTrace (list) a temporally ordered list of sets of cell
activities
@param cellCount (int) number of cells in the space being rendered
@param activityType (string) type of cell activity being displayed
@param title (string) an optional title for the figure
@param showReset (bool) if true, the first set of cell activities
after a reset will have a grayscale background
@param resetShading (float) applicable if showReset is true, specifies the
intensity of the reset background with 0.0
being white and 1.0 being black
@return (Plot) plot
"""
plot = Plot(self, title)
resetTrace = self.mmGetTraceResets().data
data = numpy.zeros((cellCount, 1))
for i in xrange(len(cellTrace)):
# Set up a "background" vector that is shaded or blank
if showReset and resetTrace[i]:
activity = numpy.ones((cellCount, 1)) * resetShading
else:
activity = numpy.zeros((cellCount, 1))
activeIndices = cellTrace[i]
activity[list(activeIndices)] = 1
data = numpy.concatenate((data, activity), 1)
plot.add2DArray(data, xlabel="Time", ylabel=activityType, name=title)
return plot
def mmGetCellTracePlot(self, cellTrace, cellCount, activityType, title="",
showReset=False, resetShading=0.25):
"""
Returns plot of the cell activity. Note that if many timesteps of
activities are input, matplotlib's image interpolation may omit activities
(columns in the image).
@param cellTrace (list) a temporally ordered list of sets of cell
activities
@param cellCount (int) number of cells in the space being rendered
@param activityType (string) type of cell activity being displayed
@param title (string) an optional title for the figure
@param showReset (bool) if true, the first set of cell activities
after a reset will have a grayscale background
@param resetShading (float) applicable if showReset is true, specifies the
intensity of the reset background with 0.0
being white and 1.0 being black
@return (Plot) plot
"""
plot = Plot(self, title)
resetTrace = self.mmGetTraceResets().data
data = numpy.zeros((cellCount, 1))
for i in xrange(len(cellTrace)):
# Set up a "background" vector that is shaded or blank
if showReset and resetTrace[i]:
activity = numpy.ones((cellCount, 1)) * resetShading
else:
activity = numpy.zeros((cellCount, 1))
activeIndices = cellTrace[i]
activity[list(activeIndices)] = 1
data = numpy.concatenate((data, activity), 1)
plot.add2DArray(data, xlabel="Time", ylabel=activityType)
return plot
def mmGetPlotUnionSDRDutyCycle(self, title="Union SDR Duty Cycle"):
"""
@return (Plot) Plot of union SDR duty cycle.
"""
plot = Plot(self, title)
unionSDRDutyCycle = self.mmGetDataUnionSDRDutyCycle()
plot.addGraph(sorted(unionSDRDutyCycle, reverse=True),
position=211,
xlabel="Union SDR Bit", ylabel="Duty Cycle")
plot.addHistogram(unionSDRDutyCycle,
position=212,
bins=len(unionSDRDutyCycle) / 10,
xlabel="Duty Cycle", ylabel="# Union SDR Bits")
return plot
def mmGetPlotConnectionsPerColumn(self, title=None):
"""
Returns plot of # connections per column.
@return (Plot) plot
"""
plot = Plot(self, title)
plot.addGraph(sorted(self._connectedCounts.tolist(), reverse=True),
position=211,
xlabel="column", ylabel="# connections")
plot.addHistogram(self._connectedCounts.tolist(),
position=212,
bins=len(self._connectedCounts) / 10,
xlabel="# connections", ylabel="# columns")
return plot
def mmGetPlotBitlife(self, title="Bitlife Statistics"):
"""
@return (Plot) Plot of bitlife statistics.
"""
plot = Plot(self, title)
bitlife = self.mmGetDataBitlife()
print bitlife
plot.addGraph(sorted(bitlife, reverse=True),
position=211,
xlabel="Union SDR Bit", ylabel="Bitlife")
plot.addHistogram(bitlife,
position=212,
bins=max(len(bitlife) / 10, 3),
xlabel="Bitlife", ylabel="# Union SDR Bits")
return plot
def mmGetPlotDistinctness(self, title="Distinctiness", showReset=False,
resetShading=0.25):
"""
Returns plot of the overlap metric between union SDRs between sequences.
@param title an optional title for the figure
@return (Plot) plot
"""
plot = Plot(self, title)
self._mmComputeSequenceRepresentationData()
data = self._mmData["distinctnessConfusion"]
plot.addGraph(sorted(data, reverse=True),
position=211,
xlabel="Time steps", ylabel="Overlap")
plot.addHistogram(data,
position=212,
bins=100,
xlabel="Overlap", ylabel="# time steps")
return plot
def mmGetPlotConnectionsPerColumn(self, title="Connections per Columns"):
"""
Returns plot of # connections per column.
@return (Plot) plot
"""
plot = Plot(self, title)
connectedCounts = numpy.ndarray(self.getNumColumns(), dtype=uintType)
self.getConnectedCounts(connectedCounts)
plot.addGraph(sorted(connectedCounts.tolist(), reverse=True),
position=211,
xlabel="column", ylabel="# connections")
plot.addHistogram(connectedCounts.tolist(),
position=212,
bins=len(connectedCounts) / 10,
xlabel="# connections", ylabel="# columns")
return plot
