"""
Fisher's Linear Discriminant
Fisher's linear discriminant (FLD) is another popular linear classifier. Fisher defined the separation between two distributions to be the ratio of the variance between the classes to the variance within the classes, which is, in some sense, a measure of the signal-to-noise ratio for the class labeling. FLD finds a linear combination of features which maximizes the separation after the projection. The resulting combination may be used for dimensionality reduction before later classification.
"""


from smile.data import AttributeDataset,NominalAttribute
from smile.data.parser import DelimitedTextParser,IOUtils
from java.io import File
from jhplot import *
from smile.classification import  FLD 
from jarray import zeros,array
from java.awt import Color
import java


# this function extract data[][] and label[] array from datasets
def getJavaArrays(dataset):
    rows=dataset.size()
    lst = [0.0]*rows
    twoDimArr = array([lst,[]], java.lang.Class.forName('[D'))
    data = dataset.toArray(twoDimArr)
    label = dataset.toArray(zeros(rows, "i"))
    return data,label


http="http://datamelt.org/examples/data/classification/toy/"
print "Reading data from",http
datasource="toy-train.txt"  
datasetName="Toy"
print Web.get(http+datasource)
parser =DelimitedTextParser()
parser.setDelimiter("[\t ]+")
parser.setResponseIndex(NominalAttribute("class"), 0)
dataset=parser.parse(datasetName,File(datasource))
print dataset
print "RDA training for classification.."

alpha=1
data,label=getJavaArrays(dataset)
fisher = FLD(data, label)

# now we will use another sample for prediction
print "Training finished. Get a new sample for predictions.."
datasource="toy-test.txt" #  (20k events) 
datasetName="Testing"
print Web.get(http+datasource)
dataset=parser.parse(datasetName,File(datasource))
data,label=getJavaArrays(dataset)

#print label
#print type(data)
prediction=fisher.predict(data)
rows=dataset.size()


c1 = HPlot("Canvas",800,450,2,1)
c1.visible()
c1.setAutoRange()
c1.setGTitle("Fisher's Linear Disiminant (FLD)") 

def applyStyle(p1,color,t):
      p1.setSymbol(t); p1.setColor(color); p1.setSymbolSize(1);

p1=P1D("Input true"); applyStyle(p1,Color.blue, 11)
p2=P1D("Input false"); applyStyle(p2,Color.gray, 4)
p1p=P1D("Predicted true"); applyStyle(p1p,Color.blue, 11)
p2p=P1D("Predicted false"); applyStyle(p2p,Color.gray, 4)

e=0.0
for i in range(rows):
    minput=data[i].tolist()
    expected=label[i]
    predicted=prediction[i] 
    if (expected>0): p1.add(minput[0],minput[1]) 
    else:            p2.add(minput[0],minput[1])
    if (predicted>0): p1p.add(minput[0],minput[1])
    else:             p2p.add(minput[0],minput[1])
    if (i%100==0): print "Input=",minput, " outcome=",expected," prediction=",predicted
    if (expected != predicted): e=e+1.0
e=e/rows
print "Error rate=",e

c1.cd(1,1)
c1.draw([p1,p2])
c1.cd(2,1)
c1.setAutoRange()
c1.draw([p1p,p2p])