36 " beta := (sum(x * y) - sum(x) * sum(y) / x[]) / " 37 " (sum(x^2) - sum(x)^2 / x[]); " 39 " alpha := avg(y) - beta * avg(x); " 41 " rmse := sqrt(sum((beta * x + alpha - y)^2) / y[]); " 50 T
x[] = {T( 1), T( 2), T(3), T( 4), T( 5), T(6), T( 7), T( 8), T( 9), T(10)};
51 T y[] = {T(8.7), T(6.8), T(6), T(5.6), T(3.8), T(3), T(2.4), T(1.7), T(0.4), T(-1)};
59 symbol_table.add_variable(
"beta" ,beta );
60 symbol_table.add_variable(
"rmse" ,rmse );
61 symbol_table.add_vector (
"x" ,
x );
62 symbol_table.add_vector (
"y" ,y );
68 parser.compile(linear_least_squares_program,expression);
72 printf(
"alpha: %15.12f\n",alpha);
73 printf(
"beta: %15.12f\n",beta );
74 printf(
"rmse: %15.12f\n",rmse );
75 printf(
"y = %15.12fx + %15.12f\n",beta,alpha);
80 linear_least_squares<double>();