Delete LossFunctions.py

LossFunctions.py

-import numpy as np
-from scipy.interpolate import interp1d
-from iminuit.util import describe
-from Model import epsilon
-from HelperFunctions import FakeFuncCode
-import matplotlib.pyplot as plt
-    
-
-           
-                                              
-class BinnedLH:
-    
-
-    def __init__(self, f, bcs, counts, bw):
-        self.f = f
-        self.x = bcs
-        self.dx = bw
-        self.counts = counts
-        self.N = np.sum(counts)
-        self.last_arg = None
-        self.func_code = FakeFuncCode(f, dock=True)
-        self.n_calls=0
-        self.eps = epsilon()
-        
-
-    def __call__(self, *arg):
-        self.last_arg = arg
-        y_hat = self.f(self.x, *arg)
-        y_hat = np.nan_to_num(y_hat, nan=self.eps, posinf=self.eps, neginf=self.eps)
-        y_hat = np.where(y_hat<self.eps, self.eps, y_hat)
-        
-        E = y_hat*self.N*self.dx
-        h = self.counts
-        mask = (h>0)
-        E = E[mask]
-        h = h[mask]
-        
-        nlogL = -np.sum(h*(np.log(E) - np.log(h)) + (h-E))
-        
-        self.n_calls+=1
-        
-
-        
-        return nlogL
-    
-    
-    
-    
-class Chi2Regression:
-    
-
-    def __init__(self, f, x, y, y_err, epsilon=1.35):
-        self.f = f
-        self.x = x
-        self.y = y
-        self.y_err = y_err
-        self.eps = np.finfo(np.float64).eps * 10
-        self.y_err[self.y_err<self.eps] = self.eps
-        self.last_arg = None
-        self.func_code = FakeFuncCode(f, dock=True)
-        self.ndof = len(self.y) - (self.func_code.co_argcount - 1)
-      
-
-    def __call__(self, *arg):
-        
-        self.last_arg = arg
-        
-        loss = ((self.f(self.x, *arg) - self.y)/(self.y_err))**2
-        
-        return np.sum(loss)
-    
-    
-    
-    
-class HuberRegression:
-    
-
-    def __init__(self, f, x, y, y_err, delta=1.345):
-        self.f = f
-        self.x = x
-        self.y = y
-        self.y_err = y_err
-        self.delta = delta
-        self.eps = np.finfo(np.float64).eps * 10
-        self.y_err[self.y_err<self.eps] = self.eps
-        self.last_arg = None
-        self.func_code = FakeFuncCode(f, dock=True)
-        self.ndof = len(self.y) - (self.func_code.co_argcount - 1)
-        
-
-    def __call__(self, *arg):
-        
-        self.last_arg = arg
-        
-        a = abs((self.y - self.f(self.x, *arg))/self.y_err)
-        cond_flag = (a>self.delta)
-        
-        loss = np.sum((~cond_flag) * (0.5 * a ** 2) - (cond_flag) * self.delta * (0.5 * self.delta - a), -1)
-        
-        return np.sum(loss)