add gradient multiple input

Author: asm-jaime

MLTests/GradientMulInOneOutTests.cs

@@ -0,0 +1,76 @@
+using FluentAssertions;
+using NUnit.Framework;
+
+namespace MLTests
+{
+    public class GradientMulInOneOutTests
+    {
+        private double WeightsSum(double[] vecA, double[] vecB)
+        {
+            double result = 0;
+            for (int i = 0; i < vecA.Length; i++)
+            {
+                result = result + vecA[i] * vecB[i];
+            }
+            return result;
+        }
+
+        private double[] WeightsSub(double[] vecA, double sub)
+        {
+            var result = new double[vecA.Length];
+            for (int i = 0; i < vecA.Length; i++)
+            {
+                result[i] = vecA[i] - sub;
+            }
+            return result;
+
+        }
+
+        private double[] WeightsMul(double[] vecA, double[] vecB)
+        {
+            var result = new double[vecA.Length];
+            for (int i = 0; i < vecA.Length; i++)
+            {
+                result[i] = vecA[i] * vecB[i];
+            }
+            return result;
+        }
+
+        private double[] GetLearnByGradientMul(double[] weights, double[] input, double predictionGoal, double alpha, double iterations)
+        {
+            var result = weights.ToArray();
+            for(int iteration = 0; iteration < iterations; ++iteration)
+            {
+                var prediction = WeightsSum(input, result);
+                var error = Math.Pow(prediction - predictionGoal, 2);
+                var delta = prediction - predictionGoal;
+                var deltaWeight = delta * prediction;
+                result = WeightsSub(result, alpha * deltaWeight);
+            }
+            return result;
+        }
+
+        [Test]
+        public void ShouldGradientLearnByMultipleInputOneOutput()
+        {
+            var weights = new double[] { 0.1, 0.2, -0.1 };
+            var alpha = 0.01;
+            var iterations = 1;
+
+            var toes = new double[] { 8.5, 9.5, 9.9, 9.0 };
+            var wlrec = new double[] { 0.65, 0.8, 0.8, 0.9 };
+            var nfans = new double[] { 1.2, 1.3, 0.5, 1.0 };
+
+            var winOrLoseData = new double[] {1, 1, 0, -1};
+
+            var predictionGoal = winOrLoseData[0];
+            var input = new double[] { toes[0], wlrec[0], nfans[0] };
+
+            var result = GetLearnByGradientMul(weights, input, predictionGoal, alpha, iterations);
+
+            result.SequenceEqual(new double[] { 0.101204, 0.20120400000000002, -0.098796000000000009 }).Should().Be(true);
+
+            //var learnWeights = 
+        }
+    }
+}