Another update from ingydotnet^djgoku

Task/Multiple-regression/Emacs-Lisp/multiple-regression.l

@@ -0,0 +1,5 @@
+(setq X1 '[0 1 2 3 4 5 6 7 8 9 10])
+(setq X2 '[0 1 1 3 3 7 6 7 3 9 8])
+(setq Y '[1 6 17 34 57 86 121 162 209 262 321])
+(calc-eval
+ (format "fit(a*X1+b*X2+c,[X1,X2],[a,b,c],[%s %s %s])" X1 X2 Y))

Task/Multiple-regression/Go/multiple-regression-1.go

@@ -0,0 +1,33 @@
+package main
+
+import (
+    "fmt"
+
+    "github.com/gonum/matrix/mat64"
+)
+
+func givens() (x, y *mat64.Dense) {
+    height := []float64{1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63,
+        1.65, 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83}
+    weight := []float64{52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93,
+        61.29, 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46}
+    degree := 2
+    x = Vandermonde(height, degree)
+    y = mat64.NewDense(len(weight), 1, weight)
+    return
+}
+
+func Vandermonde(a []float64, degree int) *mat64.Dense {
+    x := mat64.NewDense(len(a), degree+1, nil)
+    for i := range a {
+        for j, p := 0, 1.; j <= degree; j, p = j+1, p*a[i] {
+            x.Set(i, j, p)
+        }
+    }
+    return x
+}
+
+func main() {
+    x, y := givens()
+    fmt.Printf("%.4f\n", mat64.Formatted(mat64.QR(x).Solve(y)))
+}

Task/Multiple-regression/Go/multiple-regression-2.go

@@ -1,8 +1,9 @@
 package main
 
 import (
-    "code.google.com/p/gomatrix/matrix"
     "fmt"
+
+    "github.com/skelterjohn/go.matrix"
 )
 
 func givens() (x, y *matrix.DenseMatrix) {

Task/Multiple-regression/Python/multiple-regression-1.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+height = [1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63,
+    1.65, 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83]
+weight = [52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93,
+    61.29, 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46]
+
+X = np.mat(height**np.arange(3)[:, None])
+y = np.mat(weight)
+
+print(y * X.T * (X*X.T).I)

Task/Multiple-regression/Python/multiple-regression-2.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+height = [1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63,
+    1.65, 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83]
+weight = [52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93,
+    61.29, 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46]
+
+X = np.array(height)[:, None]**range(3)
+y = weight
+
+print(np.linalg.lstsq(X, y)[0])

Task/Multiple-regression/Python/multiple-regression.py

@@ -1,9 +0,0 @@
-import numpy as np
-from numpy.random import random
-
-n=100; k=10
-y = np.mat(random((1,n)))
-X = np.mat(random((k,n)))
-
-b= y * X.T * np.linalg.inv(X*X.T)
-print(b)