'''
from sklearn.linear_model import LinearRegression
from RegressionModel import RegressionModel
from MLData import MLData
from Visualization import Visualization
class LLS(RegressionModel):
"""
This class deals with K Nearest Neighbour Classifier
"""
def __init__(self):
self.regressor = LinearRegression()
if __name__ == "__main__":
data = MLData()
data.loadData("../data/mobile.csv", 2, 2)
#fit the classifier to the data
lls = LLS()
lls.fitRegressor(data)
#draw line
coef = lls.regressor.coef_.flatten().tolist() + [lls.regressor.intercept_]
print ("LLS line coef", coef)
vis = Visualization()
vis.visualizeData(data, coef)
from sklearn import svm
from MLData import MLData
from Performance import Results
from Visualization import Visualization
class SVM(ClassifierModel):
"""
This class deals with support vector machines
"""
def __init__(self):
self.classifier = svm.SVC(kernel="linear")
if __name__ == "__main__":
data = MLData()
data.loadData("../data/fish.csv", 1, 2)
#fit the classifier to the data
svm = SVM()
svm.fitClassifier(data)
#find accuracy
results = Results()
results.calculateAccuracy(svm, data)
#draw line
print("svm line", svm.classifier.coef_)
vis = Visualization()
vis.visualizeData(data, svm.classifier.coef_)