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Copy pathsoftmax_test.go
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softmax_test.go
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package linear
import (
"fmt"
"os"
"testing"
"github.com/cdipaolo/goml/base"
"github.com/stretchr/testify/assert"
)
var fdx [][]float64
var fdy []float64
var bdx [][]float64
var bdy []float64
var tdx [][]float64
var tdy []float64
// tests basically make a bunch of Softmaxs where
// when the input is above the Softmax the resultant
// output is 1.0, else 0.0
func init() {
// create the /tmp/.goml/ dir for persistance testing
// if it doesn't already exist!
err := os.MkdirAll("/tmp/.goml", os.ModePerm)
if err != nil {
panic(fmt.Sprintf("You should be able to create the directory for goml model persistance testing.\n\tError returned: %v\n", err.Error()))
}
fdx = [][]float64{}
fdy = []float64{}
for i := -2.0; i < 2.0; i += 0.2 {
for j := -2.0; j < 2.0; j += 0.2 {
for k := -2.0; k < 2.0; k += 0.2 {
fdx = append(fdx, []float64{i, j, k})
if i/2+j+2*k > 0 && -1*i-j-0.5*k > 0 {
fdy = append(fdy, 2.0)
} else if i/2+j+2*k > 0 && -1*i-j-0.5*k < 0 {
fdy = append(fdy, 1.0)
} else {
fdy = append(fdy, 0.0)
}
}
}
}
bdx = [][]float64{}
bdy = []float64{}
for i := -2.0; i < 2.0; i += 0.00073 {
bdx = append(bdx, []float64{i})
if i > 0.75 {
bdy = append(bdy, 4.0)
} else if i > 0.12 {
bdy = append(bdy, 3.0)
} else if i > -0.25 {
bdy = append(bdy, 2.0)
} else if i > -0.65 {
bdy = append(bdy, 1.0)
} else {
bdy = append(bdy, 0.0)
}
}
tdx = [][]float64{}
tdy = []float64{}
for i := -2.0; i < 2.0; i += 0.15 {
for j := -2.0; j < 2.0; j += 0.15 {
tdx = append(tdx, []float64{float64(i), float64(j)})
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
tdy = append(tdy, 2.0)
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
tdy = append(tdy, 1.0)
} else {
tdy = append(tdy, 0.0)
}
}
}
}
// maxI returns the index of the maximum value
// of a slice of float64's
func maxI(array []float64) int {
var i int
for j := range array {
if array[j] > array[i] {
i = j
}
}
return i
}
// test ( 10*i + j/20 + k ) > 0
func TestFourDimensionalSoftmaxShouldPass1(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, 1e-5, 0, 3, 10, fdx, fdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var incorrect int
var count int
for i := -1.0; i < 1.0; i += 0.30 {
for j := -1.0; j < 1.0; j += 0.30 {
for k := -1.0; k < 1.0; k += 0.30 {
guess, err = model.Predict([]float64{i, j, k})
assert.Len(t, guess, 3, "Length of Softmax hypothesis output should be 3")
prediction := maxI(guess)
if i/2+j+2*k > 0 && -1*i-j-0.5*k > 0 {
if prediction != 2 {
incorrect++
}
} else if i/2+j+2*k > 0 && -1*i-j-0.5*k < 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability (%v) should always be less than 1", val)
assert.True(t, val > -0.1, "Probability (%v) should always be greater than 0", val)
}
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
}
// same as above but with StochasticGA
func TestFourDimensionalSoftmaxShouldPass2(t *testing.T) {
var err error
model := NewSoftmax(base.StochasticGA, 1e-5, 0, 3, 10, fdx, fdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var incorrect int
var count int
for i := -1.0; i < 1.0; i += 0.3 {
for j := -1.0; j < 1.0; j += 0.3 {
for k := -1.0; k < 1.0; k += 0.3 {
guess, err = model.Predict([]float64{i, j, k})
assert.Len(t, guess, 3, "Length of Softmax hypothesis output should be 3")
prediction := maxI(guess)
if i/2+j+2*k > 0 && -1*i-j-0.5*k > 0 {
if prediction != 2 {
incorrect++
}
} else if i/2+j+2*k > 0 && -1*i-j-0.5*k < 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability (%v) should always be less than 1", val)
assert.True(t, val > -0.1, "Probability (%v) should always be greater than 0", val)
}
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
}
// test ( 10*i + j/20 + k ) > 0 but don't have enough iterations
func TestFourDimensionalSoftmaxShouldFail1(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, 1e-7, 0, 3, 1, fdx, fdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var incorrect int
var count int
for i := -1.0; i < 1.0; i += 0.3 {
for j := -1.0; j < 1.0; j += 0.3 {
for k := -1.0; k < 1.0; k += 0.3 {
guess, err = model.Predict([]float64{i, j, k})
assert.Len(t, guess, 3, "Length of Softmax hypothesis output should be 3")
prediction := maxI(guess)
if i/2+j+2*k > 0 && -1*i-j-0.5*k > 0 {
if prediction != 2 {
incorrect++
}
} else if i/2+j+2*k > 0 && -1*i-j-0.5*k < 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability (%v) should always be less than 1", val)
assert.True(t, val > -0.1, "Probability (%v) should always be greater than 0", val)
}
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) > 0.1, "Accuracy should be bad (error rate > 0.1)")
}
// same as above but with StochasticGA
func TestFourDimensionalSoftmaxShouldFail2(t *testing.T) {
var err error
model := NewSoftmax(base.StochasticGA, .000001, 0, 3, 1, fdx, fdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var incorrect int
var count int
for i := -1.0; i < 1.0; i += 0.3 {
for j := -1.0; j < 1.0; j += 0.3 {
for k := -1.0; k < 1.0; k += 0.3 {
guess, err = model.Predict([]float64{i, j, k})
assert.Len(t, guess, 3, "Length of Softmax hypothesis output should be 3")
prediction := maxI(guess)
if i/2+j+2*k > 0 && -1*i-j-0.5*k > 0 {
if prediction != 2 {
incorrect++
}
} else if i/2+j+2*k > 0 && -1*i-j-0.5*k < 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability (%v) should always be less than 1", val)
assert.True(t, val > -0.1, "Probability (%v) should always be greater than 0", val)
}
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) > 0.1, "Accuracy should be bad (error rate > 0.1)")
}
// test ( 10*i + j/20 + k ) > 0 but include an invalid data set
func TestFourDimensionalSoftmaxShouldFail3(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, 1, 0, 3, 800, [][]float64{}, fdy)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
model = NewSoftmax(base.BatchGA, 1, 0, 3, 800, [][]float64{[]float64{}, []float64{}}, fdy)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
model = NewSoftmax(base.BatchGA, 1, 0, 3, 800, nil, fdy)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
}
// same as above but with StochasticGA
func TestFourDimensionalSoftmaxShouldFail4(t *testing.T) {
var err error
model := NewSoftmax(base.StochasticGA, 1, 0, 3, 800, [][]float64{}, fdy)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
model = NewSoftmax(base.BatchGA, 1, 0, 3, 800, [][]float64{[]float64{}, []float64{}}, fdy)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
model = NewSoftmax(base.BatchGA, 1, 0, 3, 800, nil, fdy)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
}
// test ( 10*i + j/20 + k ) > 0 but include an invalid data set
func TestFourDimensionalSoftmaxShouldFail5(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, 1, 0, 3, 800, fdx, []float64{})
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
model = NewSoftmax(base.BatchGA, 1, 0, 3, 800, fdx, nil)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
}
// same as above but with StochasticGA
func TestFourDimensionalSoftmaxShouldFail6(t *testing.T) {
var err error
model := NewSoftmax(base.StochasticGA, 1, 0, 3, 800, fdx, []float64{})
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
model = NewSoftmax(base.BatchGA, 1, 0, 3, 800, fdx, nil)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
}
// invalid method
func TestFourDimensionalSoftmaxShouldFail7(t *testing.T) {
var err error
model := NewSoftmax(base.OptimizationMethod("Not A Method!!!"), 1, 0, 3, 800, fdx, fdy)
err = model.Learn()
assert.NotNil(t, err, "Learning error should not be nil")
}
func TestTwoDimensionalSoftmaxShouldPass1(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, .0001, 0, 5, 10, bdx, bdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var count int
var incorrect int
for i := -2.0; i < 2; i += 0.0372345432 {
guess, err = model.Predict([]float64{float64(i)})
prediction := maxI(guess)
if i > 0.75 {
if prediction != 4 {
incorrect++
}
} else if i > 0.12 {
if prediction != 3 {
incorrect++
}
} else if i > -0.25 {
if prediction != 2 {
incorrect++
}
} else if i > -0.65 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability should always be less than 1")
assert.True(t, val > -0.1, "Probability should always be greater than 0")
}
assert.Len(t, guess, 5, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.35, "Accuracy should be greater than 65% (this is a more challenging model)")
}
// same as above but with StochasticGA
func TestTwoDimensionalSoftmaxShouldPass2(t *testing.T) {
var err error
model := NewSoftmax(base.StochasticGA, .0001, 0, 5, 10, bdx, bdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var count int
var incorrect int
for i := -2.0; i < 2; i += 0.0372345432 {
guess, err = model.Predict([]float64{float64(i)})
prediction := maxI(guess)
if i > 0.75 {
if prediction != 4 {
incorrect++
}
} else if i > 0.12 {
if prediction != 3 {
incorrect++
}
} else if i > -0.25 {
if prediction != 2 {
incorrect++
}
} else if i > -0.65 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability should always be less than 1")
assert.True(t, val > -0.1, "Probability should always be greater than 0")
}
assert.Len(t, guess, 5, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.35, "Accuracy should be greater than 65% (this is a more challenging model)")
}
// regularization term too large
func TestTwoDimensionalSoftmaxShouldFail1(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, 1e-4, 20, 5, 10, bdx, bdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var count int
var incorrect int
for i := -1.0; i < 1; i += 0.037 {
guess, err = model.Predict([]float64{float64(i)})
prediction := maxI(guess)
if i > 0.75 {
if prediction != 4 {
incorrect++
}
} else if i > 0.12 {
if prediction != 3 {
incorrect++
}
} else if i > -0.25 {
if prediction != 2 {
incorrect++
}
} else if i > -0.65 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability should always be less than 1")
assert.True(t, val > -0.1, "Probability should always be greater than 0")
}
assert.Len(t, guess, 5, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) > 0.4, "Accuracy should be worse than 60%")
}
// same as above but with StochasticGA
func TestTwoDimensionalSoftmaxShouldFail2(t *testing.T) {
var err error
model := NewSoftmax(base.StochasticGA, 1e-5, 2, 5, 10, bdx, bdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var count int
var incorrect int
for i := -1.0; i < 1; i += 0.037 {
guess, err = model.Predict([]float64{float64(i)})
prediction := maxI(guess)
if i > 0.75 {
if prediction != 4 {
incorrect++
}
} else if i > 0.12 {
if prediction != 3 {
incorrect++
}
} else if i > -0.25 {
if prediction != 2 {
incorrect++
}
} else if i > -0.65 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
for _, val := range guess {
assert.True(t, val < 1.1, "Probability (%v) should always be less than 1", val)
assert.True(t, val > -0.1, "Probability (%v) should always be greater than 0", val)
}
assert.Len(t, guess, 5, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) > 0.4, "Accuracy should be worse than 60%")
}
// test i+j > 5
func TestThreeDimensionalSoftmaxShouldPass1(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, 5e-5, 0, 3, 500, tdx, tdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var count int
var incorrect int
for i := -1.0; i < 1.0; i += 0.112 {
for j := -1.0; j < 1.0; j += 0.112 {
guess, err = model.Predict([]float64{float64(i), float64(j)})
prediction := maxI(guess)
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
if prediction != 2 {
incorrect++
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
assert.Len(t, guess, 3, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
}
// same as above but with StochasticGA
func TestThreeDimensionalSoftmaxShouldPass2(t *testing.T) {
var err error
model := NewSoftmax(base.StochasticGA, 5e-5, 0, 3, 500, tdx, tdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var count int
var incorrect int
for i := -1.0; i < 1.0; i += 0.112 {
for j := -1.0; j < 1.0; j += 0.112 {
guess, err = model.Predict([]float64{float64(i), float64(j)})
prediction := maxI(guess)
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
if prediction != 2 {
incorrect++
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
assert.Len(t, guess, 3, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
}
//* Test Online Learning through channels *//
func TestThreeDimensionalSoftmaxOnlineShouldPass2(t *testing.T) {
// create the channel of data and errors
stream := make(chan base.Datapoint, 100)
errors := make(chan error, 20)
model := NewSoftmax(base.StochasticGA, 5e-5, 0, 3, 0, nil, nil, 2)
go model.OnlineLearn(errors, stream, func(theta [][]float64) {})
// start passing data to our datastream
//
// we could have data already in our channel
// when we instantiated the Perceptron, though
go func() {
for iter := 0; iter < 3; iter++ {
for i := -2.0; i < 2.0; i += 0.15 {
for j := -2.0; j < 2.0; j += 0.15 {
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
stream <- base.Datapoint{
X: []float64{float64(i), float64(j)},
Y: []float64{2.0},
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
stream <- base.Datapoint{
X: []float64{float64(i), float64(j)},
Y: []float64{1.0},
}
} else {
stream <- base.Datapoint{
X: []float64{float64(i), float64(j)},
Y: []float64{0.0},
}
}
}
}
}
// close the dataset
close(stream)
}()
for {
err, more := <-errors
assert.Nil(t, err, "There should not be any errors!")
assert.False(t, more, "There should not be any errors!")
if !more {
break
}
}
var guess []float64
var count int
var incorrect int
var err error
for i := -1.0; i < 1.0; i += 0.113 {
for j := -1.0; j < 1.0; j += 0.113 {
guess, err = model.Predict([]float64{float64(i), float64(j)})
prediction := maxI(guess)
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
if prediction != 2 {
incorrect++
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
assert.Len(t, guess, 3, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
}
func TestThreeDimensionalSoftmaxOnlineNormalizedShouldPass2(t *testing.T) {
// create the channel of data and errors
stream := make(chan base.Datapoint, 100)
errors := make(chan error, 20)
model := NewSoftmax(base.StochasticGA, 5e-5, 0, 3, 0, nil, nil, 2)
go model.OnlineLearn(errors, stream, func(theta [][]float64) {}, true)
// start passing data to our datastream
//
// we could have data already in our channel
// when we instantiated the Perceptron, though
go func() {
for iter := 0; iter < 3; iter++ {
for i := -1.25; i < 1.25; i += 0.15 {
for j := -1.25; j < 1.25; j += 0.15 {
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
stream <- base.Datapoint{
X: []float64{float64(i), float64(j)},
Y: []float64{2.0},
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
stream <- base.Datapoint{
X: []float64{float64(i), float64(j)},
Y: []float64{1.0},
}
} else {
stream <- base.Datapoint{
X: []float64{float64(i), float64(j)},
Y: []float64{0.0},
}
}
}
}
}
// close the dataset
close(stream)
}()
for {
err, more := <-errors
assert.Nil(t, err, "There should not be any errors!")
assert.False(t, more, "There should not be any errors!")
if !more {
break
}
}
var guess []float64
var count int
var incorrect int
var err error
for i := -1.0; i < 1.0; i += 0.113 {
for j := -1.0; j < 1.0; j += 0.113 {
guess, err = model.Predict([]float64{float64(i), float64(j)}, true)
prediction := maxI(guess)
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
if prediction != 2 {
incorrect++
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
assert.Len(t, guess, 3, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
}
//* Test Model Persistance to File *//
// test persisting y=x to file
func TestPersistSoftmaxShouldPass1(t *testing.T) {
var err error
model := NewSoftmax(base.BatchGA, 5e-5, 0, 3, 500, tdx, tdy)
err = model.Learn()
assert.Nil(t, err, "Learning error should be nil")
var guess []float64
var count int
var incorrect int
for i := -1.0; i < 1.0; i += 0.112 {
for j := -1.0; j < 1.0; j += 0.112 {
guess, err = model.Predict([]float64{float64(i), float64(j)})
prediction := maxI(guess)
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
if prediction != 2 {
incorrect++
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
assert.Len(t, guess, 3, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
// not that we know it works, try persisting to file,
// then resetting the parameter vector theta, then
// restoring it and testing that predictions are correct
// again.
err = model.PersistToFile("/tmp/.goml/Softmax.json")
assert.Nil(t, err, "Persistance error should be nil")
length := len(model.Parameters[0])
model.Parameters = make([][]float64, len(model.Parameters))
for i := range model.Parameters {
model.Parameters[i] = make([]float64, length)
}
// make sure it WONT work now that we reset theta
//
// the result of Theta transpose * X should always
// be 0.2 because the probablility is neutral
for i := -20; i < 20; i++ {
guess, err = model.Predict([]float64{float64(i), 0.2})
assert.Len(t, guess, 3, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
for j := range guess {
assert.InDelta(t, 0.333, guess[j], 1e-2, "Guess[%v] should all be equal", j)
}
}
err = model.RestoreFromFile("/tmp/.goml/Softmax.json")
assert.Nil(t, err, "Persistance error should be nil")
for i := -1.0; i < 1.0; i += 0.112 {
for j := -1.0; j < 1.0; j += 0.112 {
guess, err = model.Predict([]float64{float64(i), float64(j)})
prediction := maxI(guess)
if -2*i+j/2-0.5 > 0 && -1*i-j < 0 {
if prediction != 2 {
incorrect++
}
} else if -2*i+j/2-0.5 > 0 && -1*i-j > 0 {
if prediction != 1 {
incorrect++
}
} else {
if prediction != 0 {
incorrect++
}
}
assert.Len(t, guess, 3, "Length of a Softmax model output from hypothesis should reflect the input dimensions")
assert.Nil(t, err, "Prediction error should be nil")
count++
}
}
fmt.Printf("Predictions: %v\n\tIncorrect: %v\n\tAccuracy Rate: %v percent\n", count, incorrect, 100*(1.0-float64(incorrect)/float64(count)))
assert.True(t, float64(incorrect)/float64(count) < 0.14, "Accuracy should be greater than 86%")
}