def loadData(self):
"""
read the trainingset and testset from the csv files
and prepare the solver
"""
# Read data from given csv file
data = readCsvData(self.trainFile)
# Read test data
testdata = readCsvData(self.testFile)
# prepare the solver with the data
self.solver.loadData(data, testdata)
def crossValidate(self, splitAt = 0.7):
"""
this function takes the training set where the right answer is known,
splits it 70/30, takes the bigger part as the normal training set and
the smaller part as test set. afterwards it compares the answer from
the solver with the known right answer
"""
#load the training set from the csv file
data = readCsvData(self.trainFile)
#define where to split the dataset. Rounds down
splitter = (int)(splitAt * len(data))
trainData = data[:splitter]
validationData = data[splitter:]
# run the solving algorythm
# use the validation set without the solution in the first row
self.solver.loadData(trainData, validationData[:,1:])
self.sol = self.solver.solve()
self.validationData = validationData
# check the solution
rightSol = validationData[:,0] == self.sol[:,1]
rightPercent = np.bincount(rightSol)[1] / len(rightSol)
return rightPercent, rightSol
