nelder_mead.rb in mopti-0.2.0

- old
+ new

@@ -23,11 +23,11 @@
   #   #  :n_fev=>165,
   #   #  :n_iter=>84,
   #   #  :fnc=>5.694864987422661e-13}
   #
   # *Reference*
-  # 1. F. Gao and L. Han, "Implementing the Nelder-Mead simplex algorithm with adaptive parameters," Computational Optimization and Applications, vol. 51 (1), pp. 259--277, 2012.
+  # 1. Gao, F. and Han, L., "Implementing the Nelder-Mead simplex algorithm with adaptive parameters," Computational Optimization and Applications, vol. 51 (1), pp. 259--277, 2012.
   class NelderMead
     include Enumerable
 
     # Create a new optimizer with the Nelder-Mead simplex method.
     #
@@ -36,11 +36,11 @@
     # @param x_init [Numo::NArray] Initial point.
     # @param max_iter [Integer] Maximum number of iterations.
     #   If nil is given, max_iter sets to 200 * number of dimensions.
     # @param xtol [Float] Tolerance for termination for the optimal vector norm.
     # @param ftol [Float] Tolerance for termination for the objective function value.
-    def initialize(fnc:, args: nil, x_init:, max_iter: nil, xtol: 1e-6, ftol: 1e-6)
+    def initialize(fnc:, x_init:, args: nil, max_iter: nil, xtol: 1e-6, ftol: 1e-6)
       @fnc = fnc
       @args = args
       @x_init = x_init
       @max_iter = max_iter
       @xtol = xtol
@@ -70,17 +70,17 @@
 
       sim = x.class.zeros(n + 1, n)
       sim[0, true] = x
       n.times do |k|
         y = x.dup
-        y[k] = y[k] != 0 ? (1 + NON_ZERO_TAU) * y[k] : ZERO_TAU
+        y[k] = (y[k]).zero? ? ZERO_TAU : (1 + NON_ZERO_TAU) * y[k]
         sim[k + 1, true] = y
       end
 
       fsim = Numo::DFloat.zeros(n + 1)
 
-      (n + 1).times { |k| fsim[k] = func(sim[k, true]) }
+      (n + 1).times { |k| fsim[k] = func(sim[k, true], @args) }
       n_fev = n + 1
 
       ind = fsim.sort_index
       fsim = fsim[ind].dup
       sim = sim[ind, true].dup
@@ -89,17 +89,17 @@
       while n_iter < max_iter
         break if ((sim[1..-1, true] - sim[0, true]).abs.flatten.max <= @xtol) && ((fsim[0] - fsim[1..-1]).abs.max <= @ftol)
 
         xbar = sim[0...-1, true].sum(0) / n
         xr = xbar + alpha * (xbar - sim[-1, true])
-        fr = func(xr)
+        fr = func(xr, @args)
         n_fev += 1
 
         shrink = true
         if fr < fsim[0]
           xe = xbar + beta * (xr - xbar)
-          fe = func(xe)
+          fe = func(xe, @args)
           n_fev += 1
           shrink = false
           if fe < fr
             sim[-1, true] = xe
             fsim[-1] = fe
@@ -111,32 +111,32 @@
           shrink = false
           sim[-1, true] = xr
           fsim[-1] = fr
         elsif fr < fsim[-1]
           xoc = xbar + gamma * (xr - xbar)
-          foc = func(xoc)
+          foc = func(xoc, @args)
           n_fev += 1
           if foc <= fr
             shrink = false
             sim[-1, true] = xoc
             fsim[-1] = foc
           end
         else
           xic = xbar - gamma * (xr - xbar)
-          fic = func(xic)
+          fic = func(xic, @args)
           n_fev += 1
           if fic < fsim[-1]
             shrink = false
             sim[-1, true] = xic
             fsim[-1] = fic
           end
         end
 
         if shrink
-          (1..n).times do |j|
+          (1..n).to_a.each do |j|
             sim[j, true] = sim[0, true] + delta * (sim[j, true] - sim[0, true])
-            fsim[j] = func(sim[j, true])
+            fsim[j] = func(sim[j, true], @args)
             n_fev += 1
           end
         end
 
         ind = fsim.sort_index
@@ -154,18 +154,18 @@
 
     private_constant :NON_ZERO_TAU, :ZERO_TAU
 
     private
 
-    def func(x)
-      if @args.is_a?(Hash)
-        @fnc.call(x, **@args)
-      elsif @args.is_a?(Array)
-        @fnc.call(x, *@args)
-      elsif @args.nil?
+    def func(x, args)
+      if args.is_a?(Hash)
+        @fnc.call(x, **args)
+      elsif args.is_a?(Array)
+        @fnc.call(x, *args)
+      elsif args.nil?
         @fnc.call(x)
       else
-        @fnc.call(x, @args)
+        @fnc.call(x, args)
       end
     end
   end
 end