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js.FuncOf(src.NewLinearRegressionJS)) exports.Set("ElasticNet", js.FuncOf(src.NewElasticNetJS)) exports.Set("Lasso", js.FuncOf(src.NewLassoJS)) - return nil + exports.Set("R2Score", js.FuncOf(src.R2ScoreJS)) + return exports } func main() { diff --git a/regr/mod.ts b/regr/mod.ts index 69a9713..4d8c0cc 100644 --- a/regr/mod.ts +++ b/regr/mod.ts @@ -12,9 +12,7 @@ const wasm = wasmMmodule.instance; go.run(wasm); -const _exports = {} as Record unknown>; - // @ts-ignore: no types -__InitRegrExports(_exports); +const _exports = __InitRegrExports(_exports) as Record unknown>; export default _exports; diff --git a/regr/src/CDescent.go b/regr/src/CDescent.go index a65d6cf..d21a655 100644 --- a/regr/src/CDescent.go +++ b/regr/src/CDescent.go @@ -41,7 +41,6 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. RNorm, wNorm, ANorm, nn, tmpfactor := 0., 0., 0., 0., 0. XtA = mat.NewDense(NFeatures, NTasks, nil) - // # norm_cols_X = (np.asarray(X) ** 2).sum(axis=0) normColsX := make([]float64, NFeatures) Xmat := X.RawMatrix() for jX := 0; jX < Xmat.Rows*Xmat.Stride; jX = jX + Xmat.Stride { @@ -49,18 +48,13 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. normColsX[i] += v * v } } - // # R = Y - np.dot(X, W.T) R.Mul(X, w) R.Sub(Y, R) - - // # tol = tol * linalg.norm(Y, ord='fro') ** 2 - Y2.MulElem(Y, Y) tol *= mat.Sum(Y2) for nIter = 0; nIter < maxIter; nIter++ { wmax, dwmax = 0., 0. - for fIter = 0; fIter < NFeatures; fIter++ { if random { if rng != nil { @@ -74,23 +68,13 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. if normColsX[ii] == 0. { continue } - // # w_ii = W[:, ii] # Store previous value wii.CopyVec(w.RowView(ii)) // store previous value - - // if np.sum(w_ii ** 2) != 0.0: # can do better if mat.Norm(wii, 2) != 0. { - // # R += X[:,ii] *wii # rank 1 update - xw.Mul(X.ColView(ii), wii.T()) R.Add(R, xw) } - - // # tmp = np.dot(X[:, ii][None, :], R).ravel() tmp.MulVec(R.T(), X.ColView(ii)) - - // # nn = sqrt(np.sum(tmp ** 2)) nn = mat.Norm(tmp, 2) - // # W[:, ii] = tmp * fmax(1. - l1_reg / nn, 0) / (norm_cols_X[ii] + l2_reg) if l1reg < nn { tmpfactor = math.Max(1.-l1reg/nn, 0) / (normColsX[ii] + l2reg) @@ -98,16 +82,10 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. tmpfactor = 0. } w.RowView(ii).(*mat.VecDense).ScaleVec(tmpfactor, tmp) - - // # if np.sum(W[:, ii] ** 2) != 0.0: # can do better - if mat.Norm(w.RowView(ii), 2) != 0. { - // # R -= np.dot(X[:, ii][:, None], W[:, ii][None, :]) - // # Update residual : rank 1 update xw.Mul(X.ColView(ii), w.RowView(ii).T()) R.Sub(R, xw) } - // # update the maximum absolute coefficient update dwii = 0. for o := 0; o < NTasks; o++ { v := math.Abs(w.At(ii, o) - wii.AtVec(o)) @@ -136,11 +114,6 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. } if wmax == 0. || dwmax/wmax < dwtol || nIter == maxIter-1 { - // # the biggest coordinate update of this iteration was smaller - // # than the tolerance: check the duality gap as ultimate - // # stopping criterion - - // # XtA = np.dot(X.T, R) - l2_reg * W.T XtA.Mul(X.T(), R) XtAmat := XtA.RawMatrix() Wmat := w.RawMatrix() @@ -149,8 +122,6 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. XtAmat.Data[jXtA+i] -= l2reg * Wmat.Data[jW+i] } } - - // # dual_norm_XtA = np.max(np.sqrt(np.sum(XtA ** 2, axis=1))) dualNormXtA = 0. for ii := 0; ii < NFeatures; ii++ { XtAaxis1norm = mat.Norm(XtA.RowView(ii), 2) @@ -158,8 +129,6 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. dualNormXtA = XtAaxis1norm } } - // # R_norm = linalg.norm(R, ord='fro') - // # w_norm = linalg.norm(W, ord='fro') RNorm = mat.Norm(R, 2) wNorm = mat.Norm(w, 2) if dualNormXtA > l1reg { @@ -170,19 +139,12 @@ func CoordinateDescent(w *mat.Dense, l1reg, l2reg float64, X *mat.Dense, Y *mat. cons = 1. gap = RNorm * RNorm } - // # ry_sum = np.sum(R * y) RY.MulElem(R, Y) - // # l21_norm = np.sqrt(np.sum(W ** 2, axis=0)).sum() l21norm = 0. for ii = 0; ii < NFeatures; ii++ { l21norm += mat.Norm(w.RowView(ii), 2) } gap += l1reg*l21norm - cons*mat.Sum(RY) + .5*l2reg*(1.+cons*cons)*(wNorm*wNorm) - - // fmt.Printf("X\n%4f\nR:\n%4f\nW:\n%.4f\nXtA:\n%4f\n", mat.Formatted(X), mat.Formatted(R), mat.Formatted(w), mat.Formatted(XtA)) - // fmt.Println("dwmax", dwmax, "wmax", wmax, "dwtol", dwtol) - // fmt.Println(nIter, gap, "l1reg", l1reg, "l2reg", l2reg, "l21norm", l21norm, "sumRY", cons*mat.Sum(RY), "dualNormXtA", dualNormXtA, "RNorm", RNorm, "gap", gap) - if gap < tol { // return if we have reached the desired tolerance break diff --git a/regr/src/LinearRegression.go b/regr/src/LinearRegression.go index 9d66994..2109287 100644 --- a/regr/src/LinearRegression.go +++ b/regr/src/LinearRegression.go @@ -61,3 +61,10 @@ func NewLinearRegressionJS(this js.Value, args []js.Value) interface{} { })) return obj } + +func R2ScoreJS(this js.Value, args []js.Value) interface{} { + X := JSFloatArray2D(args[0]) + Y := JSFloatArray2D(args[1]) + reg := new(LinearRegression) + return reg.Score(X, Y) +} diff --git a/regr/src/LogisticRegression.go b/regr/src/LogisticRegression.go new file mode 100644 index 0000000..1745bbd --- /dev/null +++ b/regr/src/LogisticRegression.go @@ -0,0 +1,79 @@ +package src + +import ( + "math" + + "gonum.org/v1/gonum/mat" +) + +type LogisticRegression struct { + Epochs int + Weights *mat.Dense + Bias float64 + Losses []float64 +} + +func sigmoidFunction(x float64) float64 { + if x < 0 { + return 1. - 1./(1.+math.Exp(x)) + } + return 1. / (1. + math.Exp(-x)) +} + +func (regr *LogisticRegression) backprop(x, y mat.Matrix) float64 { + _, c := x.Dims() + ry, cy := y.Dims() + regr.Bias = 0.1 + regr.Weights = mat.NewDense(cy, c, nil) + coef := &mat.Dense{} + + coef.Mul(regr.Weights, x.T()) + coef.Apply(func(i, j int, v float64) float64 { + return sigmoidFunction(v + regr.Bias) + }, coef) + + diff := &mat.Dense{} + diff.Sub(y.T(), coef) + + w := &mat.Dense{} + w.Mul(diff, x) + regr.Weights = w + + regr.Bias -= 0.1 * (mat.Sum(diff) / float64(c)) + + // Loss + yZeroLoss := &mat.Dense{} + yZeroLoss.Apply(func(i, j int, v float64) float64 { + return v * math.Log1p(coef.At(i, j)+1e-9) + }, y.T()) + + yOneLoss := &mat.Dense{} + yOneLoss.Apply(func(i, j int, v float64) float64 { + return (1. - v) * math.Log1p(1.-coef.At(i, j)+1e-9) + }, y.T()) + + sum := &mat.Dense{} + sum.Add(yZeroLoss, yOneLoss) + return mat.Sum(sum) / float64(ry+cy) +} + +func (regr *LogisticRegression) Fit(X, Y mat.Matrix, epochs int) error { + for i := 0; i < epochs; i++ { + loss := regr.backprop(X, Y) + regr.Losses = append(regr.Losses, loss) + } + return nil +} + +func (regr *LogisticRegression) Predict(X mat.Matrix) mat.Matrix { + coef := &mat.Dense{} + coef.Mul(X, regr.Weights.T()) + coef.Apply(func(i, j int, v float64) float64 { + p := sigmoidFunction(v + regr.Bias) + if p > .5 { + return 1. + } + return 0. + }, coef) + return coef +} diff --git a/regr/src/LogisticRegression_test.go b/regr/src/LogisticRegression_test.go new file mode 100644 index 0000000..107a5ca --- /dev/null +++ b/regr/src/LogisticRegression_test.go @@ -0,0 +1,21 @@ +package src + +import ( + "fmt" + "testing" +) + +func TestLogisticRegression(t *testing.T) { + X := [][]float64{{10.1, 10.1, 10.1}, {2.1, 2.1, 2.1}, {10.2, 10.2, 10.2}, {2.2, 2.2, 2.2}} + Y := [][]float64{{0}, {1}, {0}, {1}} + XDense := Array2DToDense(X) + YDense := Array2DToDense(Y) + epochs := 10 + regr := &LogisticRegression{} + err := regr.Fit(XDense, YDense, epochs) + if err != nil { + t.Error(err) + } + fmt.Println(regr.Weights, regr.Bias, regr.Losses) + fmt.Println(YDense, regr.Predict(XDense)) +} diff --git a/regr/src/utils.go b/regr/src/utils.go index e576adc..b7dbf3a 100644 --- a/regr/src/utils.go +++ b/regr/src/utils.go @@ -1,11 +1,6 @@ -//go:build js && wasm -// +build js,wasm - package src import ( - "syscall/js" - "gonum.org/v1/gonum/mat" ) @@ -16,32 +11,3 @@ func Array2DToDense(X [][]float64) *mat.Dense { } return dense } - -func JSFloatArray(arg js.Value) []float64 { - arr := make([]float64, arg.Length()) - for i := 0; i < len(arr); i++ { - arr[i] = arg.Index(i).Float() - } - return arr -} - -func JSFloatArray2D(arg js.Value) [][]float64 { - arr := make([][]float64, arg.Length()) - for i := 0; i < len(arr); i++ { - arr[i] = make([]float64, arg.Index(i).Length()) - } - for i := 0; i < len(arr); i++ { - for j := 0; j < arg.Index(i).Length(); j++ { - arr[i][j] = arg.Index(i).Index(j).Float() - } - } - return arr -} - -func ToJSArray[T any](arr []T) []interface{} { - jsArr := make([]interface{}, len(arr)) - for i, v := range arr { - jsArr[i] = v - } - return jsArr -} diff --git a/regr/src/utils_js.go b/regr/src/utils_js.go new file mode 100644 index 0000000..903470a --- /dev/null +++ b/regr/src/utils_js.go @@ -0,0 +1,37 @@ +//go:build js && wasm +// +build js,wasm + +package src + +import ( + "syscall/js" +) + +func JSFloatArray(arg js.Value) []float64 { + arr := make([]float64, arg.Length()) + for i := 0; i < len(arr); i++ { + arr[i] = arg.Index(i).Float() + } + return arr +} + +func JSFloatArray2D(arg js.Value) [][]float64 { + arr := make([][]float64, arg.Length()) + for i := 0; i < len(arr); i++ { + arr[i] = make([]float64, arg.Index(i).Length()) + } + for i := 0; i < len(arr); i++ { + for j := 0; j < arg.Index(i).Length(); j++ { + arr[i][j] = arg.Index(i).Index(j).Float() + } + } + return arr +} + +func ToJSArray[T any](arr []T) []interface{} { + jsArr := make([]interface{}, len(arr)) + for i, v := range arr { + jsArr[i] = v + } + return jsArr +}