2016 Update

Task/Polynomial-regression/00DESCRIPTION

@@ -1,11 +1,11 @@
-Find an approximating polynom of known degree for a given data.
+Find an approximating polynomial of known degree for a given data.
 
 Example:
 For input data:
  x = {0,  1,  2,  3,  4,  5,  6,   7,   8,   9,   10};
  y = {1,  6,  17, 34, 57, 86, 121, 162, 209, 262, 321};
-The approximating polynom is:
+The approximating polynomial is:
  3 x<sup>2</sup> + 2 x + 1
-Here, the polynom's coefficients are (3, 2, 1).
+Here, the polynomial's coefficients are (3, 2, 1).
 
 This task is intended as a subtask for [[Measure relative performance of sorting algorithms implementations]].

Task/Polynomial-regression/C/polynomial-regression-2.c

@@ -16,7 +16,6 @@ bool polynomialfit(int obs, int degree,
   cov = gsl_matrix_alloc(degree, degree);
 
   for(i=0; i < obs; i++) {
-    gsl_matrix_set(X, i, 0, 1.0);
     for(j=0; j < degree; j++) {
       gsl_matrix_set(X, i, j, pow(dx[i], j));
     }

Task/Polynomial-regression/J/polynomial-regression-1.j

@@ -1,3 +1,3 @@
-   X=:i.# Y=:1 6 17 34 57 86 121 162 209 262 321
-   Y (%. (^/ x:@i.@#)) X
+   Y=:1 6 17 34 57 86 121 162 209 262 321
+   (%. ^/~@x:@i.@#) Y
 1 2 3 0 0 0 0 0 0 0 0

Task/Polynomial-regression/J/polynomial-regression-2.j

@@ -1,2 +1,2 @@
-   Y (%. (i.3) ^/~ ]) X
+   Y %. (i.3) ^/~ i.#Y
 1 2 3

Task/Polynomial-regression/PARI-GP/polynomial-regression-3.pari

@@ -1,2 +1,2 @@
-lsf(X,Y,n)=my(M=matrix(#X,n,i,j,X[i]^(j-1))); Polrev(matsolve(M~*M,M~*Y~)
-lsf([0..10], [1,6,17,34,57,86,121,162,209,262,321], 3)
+lsf(X,Y,n)=my(M=matrix(#X,n+1,i,j,X[i]^(j-1))); Polrev(matsolve(M~*M,M~*Y~))
+lsf([0..10], [1,6,17,34,57,86,121,162,209,262,321], 2)

Task/Polynomial-regression/Perl-6/polynomial-regression.pl6

@@ -0,0 +1,20 @@
+use Clifford;
+
+constant @x1 = <0 1 2 3 4 5 6 7 8 9 10>;
+constant @y = <1 6 17 34 57 86 121 162 209 262 321>;
+
+constant $x0 = [+] @e[^@x1];
+constant $x1 = [+] @x1 Z* @e;
+constant $x2 = [+] @x1 »**» 2  Z* @e;
+
+constant $y  = [+] @y Z* @e;
+
+my $J = $x1 ∧ $x2;
+my $I = $x0 ∧ $J;
+
+my $I2 = ($I·$I.reversion).Real;
+
+.say for
+(($y ∧ $J)·$I.reversion)/$I2,
+(($y ∧ ($x2 ∧ $x0))·$I.reversion)/$I2,
+(($y ∧ ($x0 ∧ $x1))·$I.reversion)/$I2;