def loopPcaKnn(loop=1):
accuracy = 0
for i in range(loop):
iris_data = IrisPCA('iris_data_set/iris.data')
iris_data.randomSplit(35)
# print (np.array(iris_data.irisdata))
# print (np.array(iris_data.test_data))
# get means and Standard deviation for training data
iris_data.calTrainMeanSd(iris_data.train_data)
# print ("Mean: \n", iris_data.train_mean)
# print ("Standard deviation: \n", iris_data.train_standard_deviation)
# apply Z score normalize for training data
# print (np.array(iris_data.train_data))
iris_data.zScoreNormalize(iris_data.train_data)
# np.set_printoptions(precision=3)
# print (np.array(iris_data.train_data))
# get Projection Matrix W
iris_data.calProjectionMatrixW(number_of_conponent=2)
# print ("\nProjection Matrix W: \n", np.array(iris_data.projectionMatrixW))
# apply Z score normalize for testing data
# print (np.array(iris_data.test_data))
iris_data.zScoreNormalize(iris_data.test_data)
# print (np.array(iris_data.test_data))
new_train_data = iris_data.getProjectedData(iris_data.train_data)
new_test_data = iris_data.getProjectedData(iris_data.test_data)
# print (np.array(new_train_data))
# print (np.array(new_test_data))
knn = Knn()
# print ("Round ",i+1, " 3-NN accuracy: ", format(knn.kNearestNeighbors(new_train_data, new_test_data), ".3f"))
accuracy += knn.kNearestNeighbors(new_train_data, new_test_data)
return accuracy / loop
def loopLdaKnn(loop=1):
accuracy = 0
for i in range(loop):
iris_data = IrisLDA('iris_data_set/iris.data')
iris_data.randomSplit(35)
iris_data.getMeansForEachClass(iris_data.train_data)
iris_data.getScatterMatrices()
iris_data.getTranformMatrixW()
new_train_data = iris_data.getProjectedData(iris_data.train_data)
new_test_data = iris_data.getProjectedData(iris_data.test_data)
# print (np.array(iris_data.train_data))
# print (np.array(new_train_data))
knn = Knn()
print(
"Round ", i + 1, " 3-NN accuracy: ",
format(knn.kNearestNeighbors(new_train_data, new_test_data),
".3f"))
accuracy += knn.kNearestNeighbors(new_train_data, new_test_data)
return accuracy / loop
def icaKnnTest():
iris_data = IrisICA('iris_data_set/iris.data')
iris_data.plotIrisData('iris data before ica')
iris_data.applyIcaFromFullIris(number_components=4)
energy_of_components = iris_data.getSortedComponentEnergy()
train_data, test_data = iris_data.getTrainTestSet(energy_of_components[:2],
train_size=0.7)
iris_data.plotIrisData('iris data after ica')
knn = Knn()
print(knn.kNearestNeighbors(train_data, test_data))
plt.show()
def icaKnnLoop(loop=10):
accuracy = 0
for i in range(loop):
iris_data = IrisICA('iris_data_set/iris.data')
iris_data.applyIcaFromFullIris(number_components=4)
energy_of_components = iris_data.getSortedComponentEnergy()
train_data, test_data = iris_data.getTrainTestSet(
energy_of_components[:2], train_size=0.7)
knn = Knn()
current_accuracy = knn.kNearestNeighbors(train_data, test_data)
accuracy += current_accuracy
print('round ', i + 1, ' accuracy: ', current_accuracy)
return accuracy / loop
def loopFaKnn(loop=1):
accuracy = 0
for i in range(loop):
iris_data = IrisFA('iris_data_set/iris.data')
iris_data.randomSplit(35)
iris_data.getProjectionMatrixW(k=2)
new_train_data = iris_data.getProjectedData(iris_data.train_data)
new_test_data = iris_data.getProjectedData(iris_data.test_data)
# print (np.array(iris_data.train_data))
# print (np.array(new_train_data))
knn = Knn()
# print ("Round ",i+1, " 3-NN accuracy: ", format(knn.kNearestNeighbors(new_train_data, new_test_data), ".3f"))
accuracy += knn.kNearestNeighbors(new_train_data, new_test_data)
return accuracy / loop