func basicComputeLoop(t *testing.T, spParams SpParams) {
/*
Feed in some vectors and retrieve outputs. Ensure the right number of
columns win, that we always get binary outputs, and that nothing crashes.
*/
sp := NewSpatialPooler(spParams)
// Create a set of input vectors as well as various numpy vectors we will
// need to retrieve data from the SP
numRecords := 100
inputMatrix := make([][]bool, numRecords)
for i := range inputMatrix {
inputMatrix[i] = make([]bool, sp.numInputs)
for j := range inputMatrix[i] {
inputMatrix[i][j] = rand.Float64() > 0.8
}
}
// With learning off and no prior training we should get no winners
y := make([]bool, sp.numColumns)
for _, input := range inputMatrix {
utils.FillSliceBool(y, false)
sp.Compute(input, false, y, sp.InhibitColumns)
assert.Equal(t, 0, utils.CountTrue(y))
}
// With learning on we should get the requested number of winners
for _, input := range inputMatrix {
utils.FillSliceBool(y, false)
sp.Compute(input, true, y, sp.InhibitColumns)
assert.Equal(t, sp.NumActiveColumnsPerInhArea, utils.CountTrue(y))
}
// With learning off and some prior training we should get the requested
// number of winners
for _, input := range inputMatrix {
utils.FillSliceBool(y, false)
sp.Compute(input, false, y, sp.InhibitColumns)
assert.Equal(t, sp.NumActiveColumnsPerInhArea, utils.CountTrue(y))
}
}
func phase2(t *testing.T, bt *boostTest) {
y := make([]bool, bt.sp.numColumns)
// Do 9 training batch through the input patterns
for i := 0; i < 9; i++ {
for idx, input := range bt.x {
utils.FillSliceBool(y, false)
bt.sp.Compute(input, true, y, bt.sp.InhibitColumns)
for j, winner := range y {
if winner {
bt.winningIteration[j] = bt.sp.IterationLearnNum
}
}
bt.lastSDR[idx] = y
}
}
// The boost factor for all columns should be at 1.
assert.Equal(t, bt.sp.numColumns, utils.CountFloat64(bt.sp.boostFactors, 1), "Boost factors are not all 1")
// Roughly half of the columns should have never been active.
winners := utils.CountInt(bt.winningIteration, 0)
assert.True(t, winners >= int(0.4*float64(bt.sp.numColumns)), "More than 60% of the columns have been active")
// All the never-active columns should have duty cycle of 0
activeSum := 0.0
for idx, val := range bt.sp.activeDutyCycles {
if bt.winningIteration[idx] == 0 {
activeSum += val
}
}
assert.Equal(t, 0, activeSum, "Inactive columns have positive duty cycle.")
dutyAvg := 0.0
dutyCount := 0
for _, val := range bt.sp.activeDutyCycles {
if val > 0 {
dutyAvg += val
dutyCount++
}
}
// The average at-least-once-active columns should have duty cycle >= 0.15
// and <= 0.25
dutyAvg = dutyAvg / float64(dutyCount)
assert.True(t, dutyAvg >= 0.15, "Average on-columns duty cycle is too low.")
assert.True(t, dutyAvg <= 0.30, "Average on-columns duty cycle is too high.")
verifySDRProps(t, bt)
}
func (tp *TrivialPredictor) reset() {
for _, method := range tp.Methods {
utils.FillSliceBool(tp.State[method].ActiveState, false)
utils.FillSliceBool(tp.State[method].ActiveStateLast, false)
utils.FillSliceBool(tp.State[method].PredictedState, false)
utils.FillSliceBool(tp.State[method].PredictedStateLast, false)
utils.FillSliceFloat64(tp.State[method].Confidence, 0.0)
utils.FillSliceFloat64(tp.State[method].ConfidenceLast, 0.0)
stats := tp.InternalStats[method]
stats.NInfersSinceReset = 0
stats.CurPredictionScore = 0.0
stats.CurPredictionScore2 = 0.0
stats.FalseNegativeScoreTotal = 0.0
stats.FalsePositiveScoreTotal = 0.0
stats.CurExtra = 0.0
stats.CurMissing = 0.0
tp.InternalStats[method] = stats
}
}
func phase4(t *testing.T, bt *boostTest) {
//The boost factor for all columns that just won should be at 1.
boostAtBeg := make([]float64, len(bt.sp.boostFactors))
copy(bt.sp.boostFactors, boostAtBeg)
// Do one more iteration through the input patterns with learning OFF
y := make([]bool, bt.sp.numColumns)
for _, input := range bt.x {
utils.FillSliceBool(y, false)
bt.sp.Compute(input, false, y, bt.sp.InhibitColumns)
// The boost factor for all columns that just won should be at 1.
assert.Equal(t, utils.SumSliceFloat64(boostAtBeg), utils.SumSliceFloat64(bt.sp.boostFactors), "Boost factors changed when learning is off")
}
}
func phase1(t *testing.T, bt *boostTest) {
y := make([]bool, bt.sp.numColumns)
// Do one training batch through the input patterns
for idx, input := range bt.x {
utils.FillSliceBool(y, false)
bt.sp.Compute(input, true, y, bt.sp.InhibitColumns)
for j, winner := range y {
if winner {
bt.winningIteration[j] = bt.sp.IterationLearnNum
}
}
bt.lastSDR[idx] = y
}
//The boost factor for all columns should be at 1.
assert.Equal(t, bt.sp.numColumns, utils.CountFloat64(bt.sp.boostFactors, 1), "Boost factors are not all 1")
//At least half of the columns should have never been active.
winners := utils.CountInt(bt.winningIteration, 0)
assert.True(t, winners >= bt.sp.numColumns/2, "More than half of the columns have been active")
//All the never-active columns should have duty cycle of 0
//All the at-least-once-active columns should have duty cycle >= 0.2
activeSum := 0.0
for idx, val := range bt.sp.activeDutyCycles {
if bt.winningIteration[idx] == 0 {
//assert.Equal(t, expected, actual, ...)
activeSum += val
}
}
assert.Equal(t, 0, activeSum, "Inactive columns have positive duty cycle.")
winningMin := 100000.0
for idx, val := range bt.sp.activeDutyCycles {
if bt.winningIteration[idx] > 0 {
if val < winningMin {
winningMin = val
}
}
}
assert.True(t, winningMin >= 0.2, "Active columns have duty cycle that is too low.")
verifySDRProps(t, bt)
}
func phase3(t *testing.T, bt *boostTest) {
//Do two more training batches through the input patterns
y := make([]bool, bt.sp.numColumns)
for i := 0; i < 2; i++ {
for idx, input := range bt.x {
utils.FillSliceBool(y, false)
bt.sp.Compute(input, true, y, bt.sp.InhibitColumns)
for j, winner := range y {
if winner {
bt.winningIteration[j] = bt.sp.IterationLearnNum
}
}
bt.lastSDR[idx] = y
}
}
// The boost factor for all columns that just won should be at 1.
for idx, val := range y {
if val {
if bt.sp.boostFactors[idx] != 1 {
assert.Fail(t, "Boost factors of winning columns not 1")
}
}
}
// By now, every column should have been sufficiently boosted to win at least
// once. The number of columns that have never won should now be 0
for _, val := range bt.winningIteration {
if val == 0 {
assert.Fail(t, "Expected all columns to have won atleast once.")
}
}
// Because of the artificially induced thrashing, even the first two patterns
// should have low overlap. Verify that the first two SDR's now have little
// overlap
overlap := computeOverlap(bt.lastSDR[0], bt.lastSDR[1])
assert.True(t, overlap < 7, "First two SDR's overlap significantly when they should not")
}
func (tp *TrivialPredictor) infer(activeColumns []int) {
numColsToPredict := int(0.5 + tp.AverageDensity*float64(tp.NumOfCols))
//for method in self.methods:
for _, method := range tp.Methods {
// Copy t-1 into t
copy(tp.State[method].ActiveStateLast, tp.State[method].ActiveState)
copy(tp.State[method].PredictedStateLast, tp.State[method].PredictedState)
copy(tp.State[method].ConfidenceLast, tp.State[method].Confidence)
utils.FillSliceBool(tp.State[method].ActiveState, false)
utils.FillSliceBool(tp.State[method].PredictedState, false)
utils.FillSliceFloat64(tp.State[method].Confidence, 0.0)
for _, val := range activeColumns {
tp.State[method].ActiveState[val] = true
}
var predictedCols []int
switch method {
case Random:
// Randomly predict N columns
//predictedCols = RandomInts(numColsToPredict, tp.NumOfCols)
break
case Zeroth:
// Always predict the top N most frequent columns
var inds []int
ints.Argsort(tp.ColumnCount, inds)
predictedCols = inds[len(inds)-numColsToPredict:]
break
case Last:
// Always predict the last input
for idx, val := range tp.State[method].ActiveState {
if val {
predictedCols = append(predictedCols, idx)
}
}
break
case All:
// Always predict all columns
for i := 0; i < tp.NumOfCols; i++ {
predictedCols = append(predictedCols, i)
}
break
case Lots:
// Always predict 2 * the top N most frequent columns
numColsToPredict := mathutil.Min(2*numColsToPredict, tp.NumOfCols)
var inds []int
ints.Argsort(tp.ColumnCount, inds)
predictedCols = inds[len(inds)-numColsToPredict:]
break
default:
panic("prediction method not implemented")
}
for _, val := range predictedCols {
tp.State[method].PredictedState[val] = true
tp.State[method].Confidence[val] = 1.0
}
if tp.Verbosity > 1 {
fmt.Println("Random prediction:", method)
fmt.Println(" numColsToPredict:", numColsToPredict)
fmt.Println(predictedCols)
}
}
}
//Clears all entries
func (sm *DenseBinaryMatrix) Clear() {
utils.FillSliceBool(sm.entries, false)
}