#-----------------------------------------------------------------------------------  
# Infer.NET IronPython example: Bayes Point Machine with 2 features  
#-----------------------------------------------------------------------------------  
  
import InferNetWrapper  
from InferNetWrapper import *  
  
def bayes_point_machine():  
 print("\n\n------------------ Infer.NET Bayes Point Machine example ------------------\n");  
 # The model  
 len = Variable.New[int]()  
 j = Range(len)  
 x = Variable.Array[Vector](j)  
 y = Variable.Array[bool](j)  
 w0 = VectorGaussian(Vector.Zero(3), PositiveDefiniteMatrix.Identity(3))  
 w = Variable.Random[Vector](w0)  
 noise = 0.1  
 y[j] = Variable.GaussianFromMeanAndVariance(  
  Variable.InnerProduct(w, x[j]).Named("innerProduct"), noise) > 0  
  
 # The data  
 incomes = System.Array[float]((63, 16, 28, 55, 22, 20 ))  
 ages = System.Array[float]((38, 23, 40, 27, 18, 40 ))  
 willBuy = System.Array[bool]((True, False, True, True, False, False))  
 dataLen = willBuy.Length  
 xdata = System.Array.CreateInstance(Vector, dataLen)  
 for i in range(0, dataLen):  
 xdata[i] = Vector.FromArray(System.Array[float]((incomes[i], ages[i], 1.0)))  
  
 # Binding the data  
 x.ObservedValue = xdata  
 y.ObservedValue = willBuy  
 len.ObservedValue = dataLen  
  
 # Inferring the weights  
 ie = InferenceEngine()  
 wPosterior = ie.Infer[VectorGaussian](w)  
 print "Dist over w=\n", wPosterior  
  
 # Prediction  
 incomesTest = System.Array[float]((58, 18, 22))  
 agesTest = System.Array[float]((36, 24, 37))  
 testDataLen = incomesTest.Length  
 xtestData = System.Array.CreateInstance(Vector, testDataLen)  
 for i in range(0, testDataLen):  
 xtestData[i] = Vector.FromArray(System.Array[float]((incomesTest[i], agesTest[i], 1.0)))  
  
 jtest = Range(testDataLen)  
 xtest = Variable.Observed[Vector](xtestData, jtest)  
 wtest = Variable.Random[Vector](wPosterior)  
 ytest = Variable.Array[bool](jtest)  
 ytest[jtest] = Variable.GaussianFromMeanAndVariance(  
 Variable.InnerProduct(wtest, xtest[jtest]), noise) > 0  
 ypred = Distribution.ToArray[System.Array[Bernoulli]](ie.Infer(ytest))  
 print "Output = ", ypred[0], ",", ypred[1], ",", ypred[2]