adding new Neural network code in parallel. Strong re-factoring coming next
classes re-factoring.
Preparing Evolutionary optimization training. (added get array and set array functions to use Weights values a parameter vector to be used in with other optimization algorithms, different than back propagation)
re-factoring of several functionalities into traits for more clarity.
Added optimization from org.fjn for future use
Added masking to weight matrix
Added momentum to gradient descend
starting input data processing for procedural networks (time series extrapolation)
added first reader specialized in financial data. this reader takes data from Financial Yahoo and produces a file to be used as training set for procedural networks
first running version of IBEX35 with Procedural neural network.
Need to fix/re-defined:
PNN needs to have a special normalization scheme: the original data needs to be normalized (close, last, volume ...) and then the resulting training set can be used without normalization.
the training data contains dates that need to be treated correctly.
Training needs to be performed on subsets of date, the whole dataset training is heavy and unnecessary.
starting re-factoring
re-factoring.
Neural networks are always considered as feedForward network acting on pre-normalized data.
first version applied to IBEX35 index
added serialization to stores neural network state to file
adding plots for nn results
possible bug on the representation of data for large periods predictions
first correct extrapolation of the IBEX 35 index
re-factoring the financial related operation into a single class FinancialTimeSeriesNN
implemented computation of regression given a yahoo finance file
performing regresions on normalize slices of data