예제 #1
0
        public LevenbergMarquardt(objective_func obj_func, List <double> inputs, List <Value> modelParams, model_func model, model_func model_jac, double lambda = 0.001, double obj_error = 0.00001, int max_iter = 10000, int rnd_seed = 0)
        {
            if (inputs.Count == 0)
            {
                throw new ApplicationException("Number of input data must be > 0");
            }
            if (modelParams.Count == 0)
            {
                throw new ApplicationException("Number of model parameters must be > 0");
            }
            _obj_func = obj_func;
            _model    = model;
            _jac_func = model_jac;
            _lambda   = lambda;
            _max_iter = max_iter;
            _inputs   = new NRealMatrix(inputs.Count, 1);
            _inputs.SetArray((from input in inputs select new NDouble[] { new NDouble(input) }).ToArray());
            _outputs     = _obj_func(_inputs);
            _modelParams = modelParams;

            // initalize the weights with normal random distibution
            var seed    = new MLapack.MCJIMatrix(4, 1);
            var rndSeed = rnd_seed == 0 ? 321 : rnd_seed;

            seed.setAt(0, 0, rndSeed);
            seed.setAt(1, 0, rndSeed);
            seed.setAt(2, 0, rndSeed);
            seed.setAt(3, 0, rndSeed);

            // check if a guess has been provided
            bool modelParamInitialized = false;

            foreach (var weight in modelParams)
            {
                if (weight.X != 0)
                {
                    modelParamInitialized = true;
                }
            }
            if (modelParamInitialized)
            {
                _weights = new NRealMatrix(1, modelParams.Count);
                _weights.SetArray(new NDouble[][] { (from param in modelParams select new NDouble(param.X)).ToArray() });
            }
            else
            {
                _weights = LapackLib.Instance.RandomMatrix(RandomDistributionType.Uniform_0_1, seed, 1, modelParams.Count);
                for (int idxWeight = 0; idxWeight < _weights.Columns; idxWeight++)
                {
                    _weights[0, idxWeight] = (_weights[0, idxWeight] * 2.0 - 1.0) / Math.Sqrt(inputs.Count);
                }
            }

            _obj_error  = obj_error;
            _error      = calcError(_weights);
            _totalError = calcTotalError(_error);
            _startError = _totalError;
        }
예제 #2
0
        public LevenbergMarquardt(objective_func obj_func, List<double> inputs, List<Value> modelParams, model_func model, model_func model_jac, double lambda = 0.001, double obj_error = 0.00001, int max_iter = 10000, int rnd_seed = 0)
        {
            if (inputs.Count == 0)
                throw new ApplicationException("Number of input data must be > 0");
            if (modelParams.Count == 0)
                throw new ApplicationException("Number of model parameters must be > 0");
            _obj_func = obj_func;
            _model = model;
            _jac_func = model_jac;
            _lambda = lambda;
            _max_iter = max_iter;
            _inputs = new NRealMatrix(inputs.Count, 1);
            _inputs.SetArray((from input in inputs select new NDouble[] { new NDouble(input) } ).ToArray());
            _outputs = _obj_func(_inputs);
            _modelParams = modelParams;

            // initalize the weights with normal random distibution
            var seed = new MLapack.MCJIMatrix(4,1);
            var rndSeed = rnd_seed == 0 ? 321 : rnd_seed;
            seed.setAt(0, 0, rndSeed);
            seed.setAt(1, 0, rndSeed);
            seed.setAt(2, 0, rndSeed);
            seed.setAt(3, 0, rndSeed);

            // check if a guess has been provided
            bool modelParamInitialized = false;
            foreach (var weight in modelParams)
            {
                if (weight.X != 0)
                    modelParamInitialized = true;
            }
            if (modelParamInitialized)
            {
                _weights = new NRealMatrix(1, modelParams.Count);
                _weights.SetArray(new NDouble[][] {(from param in modelParams select new NDouble(param.X)).ToArray() });
            }
            else
            {
                _weights = LapackLib.Instance.RandomMatrix(RandomDistributionType.Uniform_0_1, seed, 1, modelParams.Count);
                for (int idxWeight = 0; idxWeight < _weights.Columns; idxWeight++)
                    _weights[0, idxWeight] = (_weights[0, idxWeight] * 2.0 - 1.0) / Math.Sqrt(inputs.Count);
            }

            _obj_error = obj_error;
            _error = calcError(_weights);
            _totalError = calcTotalError(_error);
            _startError = _totalError;
        }