#13: Examples of using --show-expected key added.

Author: Timur Gilmullin

README.md

@@ -33,16 +33,17 @@ FuzzyClassificator
 
 
 <a name="Chapter_1"></a>Introduction
---------------
+------------------------------------
 
 This program uses neural networks to solve classification problems, and uses fuzzy sets and fuzzy logic to interpreting results. FuzzyClassificator provided under the MIT License.
 
 How does it work? Let see process scheme below.
 
 ![Two Stage of Classification Process](classification_process.png "Two Stage of Classification Process")
 
+
 <a name="Chapter_2"></a>How to use
---------------
+----------------------------------
 
 FuzzyClassificator uses ethalons.dat (default) as learning data and candidates.dat (default) for classifying data (See "Preparing data" chapter).
 Work contains two steps:
@@ -121,13 +122,18 @@ In all examples below, we used an Anaconda Python interpreter when you saw keywo
     --no-fuzzy
         Add key if You doesn't want show fuzzy results, only real. Not set by default.
 
+    --show-expected
+        Show expected results in Classify mode. 
+        WARNING! Use only if your dat-file contains columns with expected results!
+
     --reload
         Add key if You want reload network from file before usage. Not set by default.
 
     -u [epochs], --update=[epochs]
         Update error status after this epochs time, 5 by default.
         This parameter affected training speed.
 
+
 <a name="Chapter_2_2_2"></a>***Work modes***
 
 Learning Mode:
@@ -169,6 +175,7 @@ Classifying Mode:
             and outputs is number of neurons in output layer
         }
 
+
 <a name="Chapter_2_2_3"></a>***Usage examples***
 
 Start learning with user's ethalon data file and neuronet options Config=(3,[3,2],2), 10 epochs, 0.1 learning rate and 0.05 momentum, epsilon is 0.01 and stop learning if errors less than 5%, update information in log every 5 epochs:
@@ -183,7 +190,8 @@ Where 'python' is full path to Pyzo Python 3.3.2 interpreter.
 
 
 <a name="Chapter_3"></a>Preparing data
---------------
+--------------------------------------
+
 
 <a name="Chapter_3_1"></a>**ethalons.dat**
 
@@ -234,21 +242,21 @@ This is default file with data set for classifying. This file contains tab-delim
 
 
 <a name="Chapter_4"></a>Report of classificating
---------------
+------------------------------------------------
 
 To classify each of input vectors You must to use --classify key. All columns are used as values of input vectors.
 
-If You train Neuronet with command:
+If you trained Neuronet with command:
 
     python FuzzyClassificator.py --ethalons ethalons.dat --learn config=3,3,2,2 epochs=1000 rate=0.1 momentum=0.05
 
-And then classificate candidates vectors with command:
+And then you classificated candidates vectors with command:
 
     python FuzzyClassificator.py --candidates candidates.dat --network network.xml --report report.txt --classify config=3,3,2,2
 
-Then You'll get *report.text* file with information that looks like this:
+Then you will get the *report.text* file with information that looks like this:
 
-    Neuronet: C:\work\projects\FuzzyClassificator\network.xml
+    Neuronet: x:\work\projects\FuzzyClassificator_dohq\network.xml
 
     FuzzyScale = {Min, Low, Med, High, Max}
         Min = <Hyperbolic(x, {'a': 8, 'c': 0, 'b': 20}), [0.0, 0.23]>
@@ -269,7 +277,8 @@ Then You'll get *report.text* file with information that looks like this:
 
 
 <a name="Chapter_5"></a>Work with API
---------------
+-------------------------------------
+
 
 <a name="Chapter_5_1"></a>**FuzzyClassificator.py**
 
@@ -302,6 +311,7 @@ The *ClassifyingMode()* method only runs calculations using the trained neural n
 
 Some examples are below.
 
+
 <a name="Chapter_5_1_1"></a>***API example to run Learning mode***
 
 When you run Learning mode in CLI, for example:
@@ -312,10 +322,10 @@ Also you can run the same Learning mode command using API:
 
     import FuzzyClassificator as FC
     import FCLogger
-     
+
     FCLogger.SetLevel("info")
     FC.epochsToUpdate = 1
-     
+
     parameters = {
         "config": "3,3,2,2",
         "epochs": 100,
@@ -324,9 +334,10 @@ Also you can run the same Learning mode command using API:
         "epsilon": 0.75,
         "stop": 1
     }
-     
+
     FC.Main(learnParameters=parameters)  # Learning mode
-  
+
+
 <a name="Chapter_5_1_2"></a>***API example to run Classifying mode***
 
 When you run Classifying mode in CLI, for example:
@@ -337,76 +348,89 @@ Also you can run the same Classifying mode command using API:
 
     import FuzzyClassificator as FC
     from FCLogger import SetLevel
-     
+
     FC.candidatesDataFile = "candidates.dat"
     FC.neuroNetworkFile = "network.xml"
     FC.reportDataFile = "report.txt"
     FC.sepSymbol = "TAB"
     SetLevel("DEBUG")
-     
+
     parameters = {
         "config": "3,3,2,2",
     }
-     
+
     FC.Main(classifyParameters=parameters)  # Classifying mode
 
+
 <a name="Chapter_5_1_3"></a>***Another API parameters***
 
-    import FuzzyClassificator as FC  # Import main classificator's module
-     
+    import FuzzyClassificator as FC  # Import main classificator's module.
+
     # Start learning or classificating mode with parameters. 
     # If parameters are not defined then CLI-parameters are used.
     FC.Main(learnParameters=None, classifyParameters=None)
-     
+
     FC.__version__  # Version of current FuzzyClassificator build.
-         
+
     FC.ethalonsDataFile  # File with ethalon data samples, 'ethalons.dat' by default.
-         
+
     FC.candidatesDataFile  # File with candidates data samples, 'candidates.dat' by default.
-         
+
     FC.neuroNetworkFile  # File with Neuro Network configuration, 'network.xml' by default.
-     
+
     FC.reportDataFile  # Report file with classification analysis, 'report.txt' by default.
-     
+
     FC.bestNetworkFile  # Where best network saved, 'best_nn.xml' by default.
-     
+
     FC.bestNetworkInfoFile  # Where information about best network saved, 'best_nn.txt' by default.
-     
+
     FC.epochsToUpdate  # Epochs between error status updated, 5 by default.
-     
+
     FC.ignoreColumns  # List of ignored columns. Empty list [] by default, e.g. FC.ignoreColumns = [1, 3].
-     
+
     FC.ignoreRows  # List of ignored rows. [1] by default, e.g. FC.ignoreRows = [1, 3].
-     
+
     FC.sepSymbol  # Separator symbol. TAB symbol '\t' used by default.
-     
+
     FC.reloadNetworkFromFile  # Reload or not Neuro Network from file before usage. False by default.
-     
+
     FC.noFuzzyOutput  # Show results with fuzzy values too if False (default). Show only real values in report if True.
 
+    FC.showExpected  # Show expected results in Classify mode. WARNING! Use only if your dat-file contains columns with expected results to avoid errors!
+
+
 <a name="Chapter_5_2"></a>**PyBrainLearning.py**
 
 This is library for work with fuzzy neural networks. You can import and re-use module in your programm if you'd like to realize own work with networks.
 
 All routines to work with fuzzy neural networks realized in *FuzzyNeuroNetwork()* class. It contains next main methods:
 
-- *ParseRawDataFile()* - used for parsing file with text raw data,
-- *PrepareDataSet()* - used for converting parsed raw-data into PyBrain dataset format,
-- *CreateNetwork()* - used for creating PyBrain network,
-- *CreateTrainer()* - used for creating PyBrain trainer,
-- *SaveNetwork()* - used for saving network in PyBrain xml-format,
-- *LoadNetwork()* - used for loading network from PyBrain xml-format file,
-- *Train()* - realize network training mechanism,
-- *CreateReport()* - creates text report after classification vector-candidates.
+    import PyBrainLearning as FL  # Import supporting module.
+
+    FL.ParseRawDataFile()  # Used for parsing file with text raw data.
+
+    FL.PrepareDataSet()  # Used for converting parsed raw-data into PyBrain dataset format.
+
+    FL.CreateNetwork()  # Used for creating PyBrain network.
+
+    FL.CreateTrainer()  # Used for creating PyBrain trainer.
+
+    FL.SaveNetwork()  # Used for saving network in PyBrain xml-format.
+
+    FL.LoadNetwork()  # Used for loading network from PyBrain xml-format file.
+
+    FL.Train()  # Realize network training mechanism.
+
+    FL.CreateReport()  # Create text report after classification vector-candidates.
 
-You can import this class and use its methods in other projects.
 
 <a name="Chapter_5_3"></a>**FuzzyRoutines.py**
 
 Library contains some routines for work with fuzzy logic operators, fuzzy datasets and fuzzy scales.
 
 There are some examples of working with fuzzy library after importing it. Just copying at the end of FuzzyRoutines and run it.
 
+
 <a name="Chapter_5_3_1"></a>***Work with membership functions***
 
 Usage of some membership functions (uncomment one of them):
@@ -444,6 +468,7 @@ Calculating some function's values in [0, 1]:
         res = funct.mju(xPar)  # calculate one value of MF with given parameters
         print('{}({:1.1f}, {}) = {:1.4f}'.format(funct.name, xPar, funct.parameters, res))
 
+
 <a name="Chapter_5_3_2"></a>***Work with fuzzy set***
 
     fuzzySet = FuzzySet(funct, (0., 1.))  # creating fuzzy set A = <mju_funct, support_set>
@@ -464,6 +489,7 @@ Calculating some function's values in [0, 1]:
     print('New value of Defuz({}) = {:1.2f}'.format(fuzzySet.name, fuzzySet.Defuz()))
     print('Printing fuzzy set after changes:', fuzzySet)
 
+
 <a name="Chapter_5_3_3"></a>***Work with fuzzy scales***
 
 Fuzzy scale is an ordered set of linguistic variables that looks like this:
@@ -509,6 +535,7 @@ Change scale levels:
     for item in scale.levels:
         print('Defuz({}) = {:1.2f}'.format(item['name'], item['fSet'].Defuz()))
 
+
 <a name="Chapter_5_3_4"></a>***Work with Universal Fuzzy Scale***
 
 Universal fuzzy scales S_f = {Min, Low, Med, High, Max} pre-defined in UniversalFuzzyScale() class.
@@ -564,6 +591,7 @@ Finding fuzzy level using GetLevelByName() function without exact matching:
     res = uniFScale.GetLevelByName('Highest', exactMatching=False)
     print("GetLevelByName('Highest', {}) = {}, {}".format(uniFScale.name, res['name'] if res else 'None', res['fSet'] if res else 'None'))
 
+
 <a name="Chapter_5_3_5"></a>***Work with fuzzy logic operators***
 
 Checks that number is in [0, 1]:
@@ -614,6 +642,7 @@ Calculates result of S-coNorm operations for N numbers, N > 2:
     print("SCoNormCompose(0.25, 0.5, 0.75, 'boundary') =", SCoNormCompose(0.25, 0.5, 0.75, normType='boundary'))
     print("SCoNormCompose(0.25, 0.5, 0.75, 'drastic') =", SCoNormCompose(0.25, 0.5, 0.75, normType='drastic'))
 
+
 <a name="Chapter_5_3_6"></a>***Output examples***
 
 If you run code above - you'll see next console output: