From b558b4a1ebd88f4cc65cc94c2b7256f335779c77 Mon Sep 17 00:00:00 2001
From: Marie-Gabrielle Dondon
 <85bc36e0a8096c618fbd5993d1cca191@app-learninglab.inria.fr>
Date: Mon, 12 Nov 2018 16:23:40 +0000
Subject: [PATCH] Replace challenger_Python_org.org

---
 src/Python3/challenger_Python_org.org | 30 ++++++++++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/src/Python3/challenger_Python_org.org b/src/Python3/challenger_Python_org.org
index 7d5351d..699e8fa 100644
--- a/src/Python3/challenger_Python_org.org
+++ b/src/Python3/challenger_Python_org.org
@@ -90,7 +90,7 @@ logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']],
 print(logmodel.summary())
  #+end_src
 
-The maximum likelyhood estimator of the intercept and of Temperature
+ The maximum likelyhood estimator of the intercept and of Temperature
  are thus *$\hat{\alpha}$ = 5.0850* and *$\hat{\beta}$ = -0.1156*. This *corresponds*
  to the values from the article of Dalal /et al./ The standard errors are 
  /$s_{\hat{\alpha}}$ = 7.477/ and /$s_{\hat{\beta}}$ = 0.115/, which is *different* from
@@ -128,6 +128,7 @@ print(logmodel.summary())
 %matplotlib inline
 data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})
 data_pred['Frequency'] = logmodel.predict(data_pred)
+
 data_pred.plot(x="Temperature",y="Frequency",kind="line",ylim=[0,1])
 plt.scatter(x=data["Temperature"],y=data["Frequency"])
 plt.grid(True)
@@ -136,6 +137,33 @@ plt.savefig(matplot_lib_filename)
 matplot_lib_filename
  #+end_src
 
+ #+begin_src python :results output :session :exports both
+print(data_pred.head())
+ #+end_src
+
+ La fonction logmodel.predict(=data_pred=) ne fonctionne pas correctement (Frequency = 1 pour toutes les
+ températures).
+
+ On peut alors utiliser le code suivant pour calculer les prédictions
+ et tracer la courbe :
+
+ #+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
+# Inspiring from http://blog.yhat.com/posts/logistic-regression-and-python.html
+%matplotlib inline
+def logit_inv(x):
+    return(np.exp(x)/(np.exp(x)+1))
+
+data_pred['Prob']=logit_inv(data_pred['Temperature'] * logmodel.params['Temperature'] + logmodel.params['Intercept'])
+print(data_pred.head())
+
+data_pred.plot(x="Temperature",y="Prob",kind="line",ylim=[0,1])
+plt.scatter(x=data["Temperature"],y=data["Frequency"])
+plt.grid(True)
+
+plt.savefig(matplot_lib_filename)
+matplot_lib_filename
+#+end_src
+
  This figure is very similar to the Figure 4 of Dalal /et al./ *I have
  managed to replicate the Figure 4 of the Dalal /et al./ article.*
 
-- 
2.18.1