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Abdullah Alnuaimi b00033811

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# -*- coding: utf-8 -*-
"""
Created on Sun Jun 24 00:26:00 2018
@author: b0003
"""
import numpy as np
np.random.seed(1337)
from NaiveBayes_log import NB
import matplotlib.pyplot as plt
"""
Univariate Naive Bayes
@author: Abdullah Al Nuaimi
"""
import numpy as np
from scipy.misc import logsumexp
class NB():
def __init__(self,x,r):
# define the input data
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
def gaussian_noise(mu,sigma,n):
return np.random.normal(mu,sigma,n)
from sklearn.preprocessing import PolynomialFeatures
degree=8
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from Poly import Poly
from sklearn.model_selection import KFold
#%% Import Data
x_raw,y_raw=np.loadtxt('data.csv',delimiter=',')
# k = 8
degree=8 # k=8
"""
Polynomial Class
@author: Abdullah Alnuaimi
"""
class Poly():
def __init__(self,a,k):
self.a=a
self.k=k
self.A =lambda x,a=a,k=k:[[a*n**k for a,k in zip(a,k)] for n in x]
self.V=None
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
def fit_poly(a,k):
'''returns a function of the dot product (A=V.a) '''
A=lambda x,a=a,k=k:[[a*n**k for a,k in zip(a,k)] for n in x]
return A
def evaluate_poly(x,A):
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# loading data
x_raw,y_raw=np.loadtxt('data.csv',delimiter=',')
# reshape the data
x=x_raw.reshape(-1,1)
y=y_raw.reshape(-1,1)
# split the model into test/train sets
"""
Polynomials
@author: Abdullah Alnuaimi
"""
import numpy as np
import matplotlib.pyplot as plt
def fit_poly(a,k):
'''returns a function of the dot product (A=V.a) '''
A=lambda x,a=a,k=k:[[a*n**k for a,k in zip(a,k)] for n in x]
return A
"""
Polynomials
@author: Abdullah Alnuaimi
"""
import numpy as np
import matplotlib.pyplot as plt
def fit_poly(a,k):
'''returns a function of the dot product (A=V.a) '''
A=lambda x,a=a,k=k:[[a*n**k for a,k in zip(a,k)] for n in x]
return A
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
model=LinearRegression()
y=y.reshape(-1,1) # reshaping the data
x=x.reshape(-1,1)
#spliting into test/train with a test size of .3 or 30 samples.
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,shuffle=True)
model.fit(x_train,y_train) # fitting the model
print(model.score(x_test,y_test)) # get the r2 score
p=model.predict(x) # generate a prediction from the model using the original input