EasyNN-plus v16.0b cracked version download

                                           EasyNN-plus v16.0b








            This is the full cracked version of the software. Download, extract, install, enjoy.
   Inside the archive there is "crack" folder wich contains everything you need to crack the software.
                                                Download link:
                      http://fileom.com/7lhtamqj0ysu/EasyNN-plus.v16.0b.cracked.rar






With EasyNN-plus complex data analysis is fast and simple.  Prediction, classification and time series projection is easy.  Create the EasyNN-plus data grids from text, csv, spreadsheet, image or binary files.  Produce multi layer neural networks from the grids.  Networks with numeric, text, image or combinations of data types are created automatically or manually using the network editor.  Train, validate and query EasyNN-plus neural networks with just a few button pushes.  See the diagrams, graphs and the input / output data displayed in detail.  Watch the nodes, the connections, the graphs and the results being updated while the network learns your data.  EasyNN-plus can interwork with other applications using the built in Script and Macro facilities.

Key Features


Data Processing

    Import Excel files

    Import CSV and TXT files

    Import bitmap image files

    Import binary files

    Extensive pre-processing facilities

    Date and Time encoding

    Integer, real, boolean, text or image modes

    Many methods of handling missing values



    Min/Max column values for scaling

    Outlier handling

    Random and manual data partitioning

    Data subsets

    Check for duplicate rows

    Column value classification

    Range extender and filler

    Shuffle rows

Building the Neural Network

    Input and output selection

    Multiple inputs and outputs



    Check rows and columns are suitable to build a network

    Automatic or manual production of hidden layers

Control Training and Validating

    Automatic or manual learning rate and momentum

    Automatic decay of learning rate and momentum

    Global or independent input and output validating rules

    Scoring rules

    Automatic or manual network reconfiguration while learning

    Stop after fixed number of cycles

    Node and Weight freezing



    Variable validating periods

    Fixed time stop

    Variable speed learning for visual demonstrations

    Many methods of early stopping

    Validating correct or within range

    Jitter and Noise

    Random and Balanced presentation

Special Files

    Save any part of the network to TXT or CSV files

    Save the data grid to TXT or CSV files



    Save learning progress to TXT or CSV files

    Save backup while learning

Auto Save while learning

    Variable save period

    Save when error reduces



    Save when validating results improve


Macros and Scripts

    Record and Play macros

    Extensive script language

    Add commands and scripts to macros



    Run scripts from the command line or other applications

    Single step macros

    Run background scripts while hidden

Querying

    Query trained networks manually

    Query networks from external files

    See output values change when changing input values

    Seek high or low outputs



    Cycle seek though all inputs

    Save results to TXT or CSV files

    Query inputs can be extended beyond the training range

    Extrapolated results can be produced

Forecasting

    Forecast future values with multiple networks

    Allow forecasts to extend beyond training limits



    Assess risk of forecasts

    Restrict forecasts to upper or lower training limits

Associations and Clusters

    Automatically find associated inputs and outputs

    Find inputs and outputs that form clusters



    Save associations and clusters

    Build networks from clusters

Leave Some Out Validating

    Sequential leave out subsets selection

    Random row selection validating

    Shuffle before validating



    Random leave out subsets selection

    Multi-fold cross validating

    Comprehensive report

Node Reduction and Weight Pruning

    Prune insignificant weights while learning



    Reduce network to minimum size

Views

    Data grid

    Network nodes and connections

    Learning progress graph

    Column values graph and trends

    Actual and predicted outputs for training and validating examples




    Training and validating examples errors

    Input importance and relative importance

    Input sensitivity and relative sensitivity

    Input and output associations

    Diagnostic array

    General information