Use new UDTs to consolidate and simplify training / validation API.

MachineLearning/src/DataModel/Interop.cs

@@ -176,8 +176,8 @@ public static List<long[]> PartialLocalLayer(long[] indices, char pauli)
 
         /// <summary>
         /// Creates a cyclic block of nQubits controlled rotations that starts
-        /// with contol qubit (nQubits-1), target qubit (cspan-1) % n , followed by the 
-        /// ladder of entanglers with control qubit iq and target qubit (iq+cspan) % n
+        /// with control qubit (nQubits-1), target qubit (cspan-1) % nQubits , followed by a
+        /// ladder of entanglers with control qubits iq and target qubit (iq+cspan) % nQubits
         /// </summary>
         /// <param name="nQubits">Number of qubits to entangle</param>
         /// <param name="pauli"></param>
@@ -220,68 +220,6 @@ public static void reindex(List<long[]> struc)
             }
         }
 
-        public void QcccTrainSequential(IQArray<IQArray<double>> parameterSource, IQArray<IQArray<double>> trainingSet, IQArray<long> trainingLabels, IQArray<IQArray<long>> trainingSchedule,
-            IQArray<IQArray<long>> validationSchedule, double learningRate, double tolerance, long miniBatchSize, long maxEpochs, long nMeasurements, uint randomizationSeed)
-        {
-            var sim = new QuantumSimulator(false, randomizationSeed);
-            (this._cachedParameters, this._bias) =
-            TrainQcccSequential.Run(sim, this._nQubits, this._structure, parameterSource, trainingSet, trainingLabels, trainingSchedule, validationSchedule, learningRate, tolerance, miniBatchSize, maxEpochs, nMeasurements).Result;
-        }
-        public void QcccTrainSequential(List<double[]> parameterSource, List<double[]> trainingSet, List<long> trainingLabels, List<long[]> trainingSchedule,
-             List<long[]> validationSchedule, double learningRate, double tolerance, long miniBatchSize, long maxEpochs, long nMeasurements, uint randomizationSeed)
-        {
-            QcccTrainSequential(Qonvert.ToQ(parameterSource), Qonvert.ToQ(trainingSet), Qonvert.ToQ(trainingLabels), Qonvert.ToQ(trainingSchedule),
-               Qonvert.ToQ(validationSchedule), learningRate, tolerance, miniBatchSize, maxEpochs, nMeasurements, randomizationSeed);
-        }
-
-        public void QcccTrainParallel(IQArray<IQArray<double>> parameterSource, IQArray<IQArray<double>> trainingSet, IQArray<long> trainingLabels, IQArray<IQArray<long>> trainingSchedule,
-            IQArray<IQArray<long>> validationSchedule, double learningRate, double tolerance, long miniBatchSize, long maxEpochs, long nMeasurements, uint randomizationSeed)
-        {
-            var simAll = new List<QuantumSimulator>(parameterSource.Count);
-            var resultsAll = new List<(IQArray<double>, double)>(parameterSource.Count);
-            var parameterComb = new List<IQArray<IQArray<double>>>(parameterSource.Count);
-
-            var indices = new int[parameterSource.Count];
-            for (int j = 0; j < parameterSource.Count; j++)
-            {
-                indices[j] = j;
-                simAll.Add(new QuantumSimulator(false, randomizationSeed));
-                resultsAll.Add((new QArray<double>(),0.0));
-                parameterComb.Add(new QArray<IQArray<double>>(new IQArray<double>[] { parameterSource[j] })); //Isolating parameter starts - one per thread
-            }
-            Parallel.ForEach(indices,
-                (j) =>
-                {
-
-                    var rslt = 
-                    TrainQcccSequential.Run(simAll[j], this._nQubits, this._structure, parameterComb[j], trainingSet, trainingLabels, trainingSchedule, validationSchedule, learningRate, tolerance, miniBatchSize, maxEpochs, nMeasurements).Result;
-                    resultsAll[j] = rslt;
-                }
-                );
-            //Estimated parameters and biases for each proposed parameter start. Now postprocess
-            long bestValidation = long.MaxValue;
-            int bestJ = -1;
-            var sim = new QuantumSimulator(false, randomizationSeed);
-            for (int j = 0; j < parameterSource.Count; j++)
-            {
-                var (pars, bias) = resultsAll[j];
-                long misses = CountValidationMisses.Run(sim, tolerance, this._nQubits, trainingSet, trainingLabels, validationSchedule, this._structure, pars, bias, nMeasurements).Result;
-                if (bestValidation > misses)
-                {
-                    bestValidation = misses;
-                    bestJ = j;
-                }
-            }
-            (this._cachedParameters, this._bias) = resultsAll[bestJ];
-        } //QcccTrainParallel
-
-        public void QcccTrainParallel(List<double[]> parameterSource, List<double[]> trainingSet, List<long> trainingLabels, List<long[]> trainingSchedule,
-             List<long[]> validationSchedule, double learningRate, double tolerance, long miniBatchSize, long maxEpochs, long nMeasurements, uint randomizationSeed)
-        {
-            QcccTrainParallel(Qonvert.ToQ(parameterSource), Qonvert.ToQ(trainingSet), Qonvert.ToQ(trainingLabels), Qonvert.ToQ(trainingSchedule),
-               Qonvert.ToQ(validationSchedule), learningRate, tolerance, miniBatchSize, maxEpochs, nMeasurements, randomizationSeed);
-        }
-
         public long CountMisclassifications(double tolerance, IQArray<IQArray<double>> samples, IQArray<long> knownLabels, IQArray<IQArray<long>> validationSchedule, long nMeasurements, uint randomizationSeed)
         {
             if (this.isTrained)
@@ -304,31 +242,6 @@ public long CountMisclassifications(double tolerance, List<double[]> samples, Li
             return CountMisclassifications(tolerance, Qonvert.ToQ(samples), Qonvert.ToQ(knownLabels), Qonvert.ToQ(validationSchedule), nMeasurements, randomizationSeed);
         }
 
-        //EstimateClassificationProbabilitiesClassicalDataAdapter(samples: Double[][], schedule: Int[][], nQubits: Int,  gates: Int[][], param: Double[], measCount: Int): Double[]
-        public double[] EstimateClassificationProbabilities(double tolerance, IQArray<IQArray<double>> samples, IQArray<IQArray<long>> schedule, long nMeasurements, uint randomizationSeed)
-        {
-            if (this.isTrained)
-            {
-                var sim = new QuantumSimulator(false, randomizationSeed);
-                IQArray<double> probs = EstimateClassificationProbabilitiesClassicalDataAdapter.Run(sim, tolerance, samples, schedule, this._nQubits, this._structure, this.CachedParameters, nMeasurements).Result;
-                return probs.ToArray();
-            }
-            return new double[] { -1.0 };          
-        }
-
-        public double[] EstimateClassificationProbabilities(double tolerance, List<double[]> samples, List<long[]> schedule, long nMeasurements, uint randomizationSeed)
-        {
-            return EstimateClassificationProbabilities(tolerance, Qonvert.ToQ(samples), Qonvert.ToQ(schedule), nMeasurements, randomizationSeed);
-        }
-
-        public double[] EstimateClassificationProbabilities(double tolerance, List<double[]> samples, long nMeasurements, uint randomizationSeed)
-        {
-            List<long[]> sched = new List<long[]>(1);
-            sched.Add(new long[] { 0L, 1L, (long)(samples.Count - 1) });
-            return EstimateClassificationProbabilities(tolerance, Qonvert.ToQ(samples), Qonvert.ToQ(sched), nMeasurements, randomizationSeed);
-        }
-
-
     } //class ClassificationModel
 
 }