From 2815a931f7a63ed288a6aebbe9609d66e69c8d5e Mon Sep 17 00:00:00 2001
From: Martin Palovic <80816673+mtpalovic@users.noreply.github.com>
Date: Sat, 19 Mar 2022 18:12:11 +0100
Subject: [PATCH] Delete p_2.py

---
 p_2.py | 167 ---------------------------------------------------------
 1 file changed, 167 deletions(-)
 delete mode 100644 p_2.py

diff --git a/p_2.py b/p_2.py
deleted file mode 100644
index 6031eb1..0000000
--- a/p_2.py
+++ /dev/null
@@ -1,167 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
-import numpy as np
-from scipy import linspace, polyval, polyfit, sqrt, stats, randn, optimize
-
-import pandas as pd
-import matplotlib.pyplot as plt
-
-import csv
-
-import scipy
-from sklearn.linear_model import LinearRegression
-
-import matplotlib as plt
-get_ipython().run_line_magic('matplotlib', 'inline')
-
-import sphinx
-
-
-# In[3]:
-
-
-class lr(object):
-    """
-    Class constructor.
-    """
-    N_ITERS: 1000
-    
-    def __init__(self,x0,y0,l):
-        """
-        Constructor method.
-        """
-        
-        self.x0 = x0
-        self.y0 = y0
-        
-        self.l = float(l)
-        #forces to be float or init, otherwise error raised
-        assert (type(l)==float or type(l)==int or type(x0)==None)
-        
-        
-        
-        self.n = len(self.x0)
-        
-        #weight,bias, not part of init
-        self.a = 0
-        self.b = 0
-        
-        self.h = np.zeros(lr.N_ITERS)
-    
-    
-    
-    
-    
-    def __str__(self):
-        """String representation of an object. Init params are instance attributes.
-        :return: init param as string
-        :rtype: str
-        """
-        return f"init params:{self.x0},{self.y0}, {self.l}"
-    
-    
-    
-    
-    
-    
-    def c(self,y_):
-        """
-        Cost function
-        :param y_: init method
-        :type y_: init method
-        
-        :return:
-        :rtype: 
-        
-        """
-        c = np.sum(np.square(self.y0 - y_))/(2*self.n)
-        return c 
-    
-    def f(self):
-        """
-        Fit function
-        :param y_: init method
-        :type y_: init method
-        
-        :return:
-        :rtype: 
-        
-        """
-        for e in range(0,len(self.iters_),1):
-            
-            y_ = self.b*self.X + self.a
-            
-            dv_a = (-2/self.n)*(self.y0 - y_)
-            
-            dv_b = (-2/self.n)*(self.x0*(self.y0 - y_))
-            
-            
-            self.a = self.a - dv_a*self.l
-            self.b = self.b - dv_b*self.l
-            
-            self.history[e] = self.c(y_)
-            self.mse = self.mean_se(self.y0, y_)
-        
-        return self.mse
-    
-    
-    def mean_se(self,y_p,y):
-        """
-        Mean squared error.
-        :param y_p: 
-        :type y_p: 
-        
-        :param y: 
-        :type y: 
-        
-        :return:
-        :rtype:
-        
-        """
-        er = y - y_p
-        mse = np.sum(np.square(er))/self.n
-        
-        return mse
-    
-    
-    
-    def create_list(self,*args):
-        self.li = [*args]
-        return self.li
-    
-    
-    def create_tuple(self):
-        """Creates a tuple
-        :return:
-        :rtype: tuple
-        """
-        self.tupl = ()
-        
-        return self.tupl
-    
-    
-     def create_dict(self):
-        """Creates a dictionary
-        :return:
-        :rtype:
-        """
-        self.dict = {}
-        
-        return self.dict
-
-
-# In[4]:
-
-
-q = lr(100,10,800)
-
-
-# In[ ]:
-
-
-
-