From b5d479505eae010868d5c42f12f24e8782c890fd Mon Sep 17 00:00:00 2001
From: Jack Christopher Hutchinson Rolph <jack.rolph@desy.de>
Date: Fri, 23 Dec 2022 19:16:42 +0100
Subject: [PATCH] Update LossFunctions.py
---
LossFunctions.py | 31 ++++++++++++++++++-------------
1 file changed, 18 insertions(+), 13 deletions(-)
diff --git a/LossFunctions.py b/LossFunctions.py
index 87eadd3..9f7fa45 100644
--- a/LossFunctions.py
+++ b/LossFunctions.py
@@ -4,9 +4,9 @@ from iminuit.util import describe
from Model import epsilon
from HelperFunctions import FakeFuncCode
import matplotlib.pyplot as plt
-
-
-
+
+
+
class BinnedLH:
@@ -14,6 +14,7 @@ class BinnedLH:
def __init__(self, f, bin_centres, counts, bw):
self.f = f
self.x = bin_centres
+ self.len_x = len(self.x)
self.dx = bw
self.counts = counts
self.N = np.sum(counts)
@@ -22,6 +23,8 @@ class BinnedLH:
self.func_code = FakeFuncCode(f, dock=True)
self.n_calls=0
self.eps = epsilon()
+ self.mask = (self.counts>0)
+
def __call__(self, *arg):
@@ -29,21 +32,23 @@ class BinnedLH:
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
+ E = y_hat*self.dx
h = self.counts
- mask = (h>0)
- E = E[mask]
- h = h[mask]
+ mask = self.mask
- nlogL = -np.sum(h*(np.log(E) - np.log(h)) + (h-E))
- self.n_calls+=1
+ nlogL= np.zeros(self.len_x)
+ nlogL[mask] = h[mask]*(np.log(E[mask]) - np.log(h[mask])) + (h[mask]-E[mask])
+ nlogL[~mask] = -E[~mask]
+
+ nlogL = -np.sum(nlogL)
+
+ self.n_calls+=1
+
return nlogL
@@ -102,4 +107,4 @@ class HuberRegression:
loss = np.sum((~cond_flag) * (0.5 * a ** 2) - (cond_flag) * self.delta * (0.5 * self.delta - a), -1)
- return np.sum(loss)
\ No newline at end of file
+ return np.sum(loss)
--
GitLab