Try solver

.gitignore

 __pycache__
 *.csv
 *.swp
+*.json

draw_ert.py

@@ -2,6 +2,7 @@
 from os import system, remove, path
 import matplotlib.pyplot as plt
 import numpy as np
+import json
 
 ########################################################################
 # Interface
@@ -18,11 +19,12 @@ if __name__=="__main__":
 	can.add_argument("-v", "--for-value", metavar="NB", type=int,
 			help="Print ERT with probabilities to obtain this value")
 
+	can.add_argument("-save", "--save-proba", action='store_true', default=False,
+            help="Disable graphical output and save proba")
+
 	arg = can.parse_known_args()
 	the = arg[0]
 
-	print(the)
-
 	# Minimum checks.
 	assert(0 < the.nb_run)
 
@@ -55,9 +57,9 @@ if __name__=="__main__":
 			if line[0] == 1:
 				if len(attempt) > 0:
 					data.append(attempt)
-				current_best = float("inf")
+				current_best = 0
 				attempt = {}
-			if line[1] < current_best:
+			if line[1] > current_best:
 				attempt[line[0]] = line[1]
 				current_best = line[1]
 
@@ -68,7 +70,7 @@ if __name__=="__main__":
 		for attempt in data:
 			good = False
 			for t,v in attempt.items():
-				if t < time and v < value:
+				if t < time and v > value:
 					good = True
 					break
 			if good:
@@ -78,7 +80,10 @@ if __name__=="__main__":
 	probability = np.vectorize(probability, excluded=[0])
 
 	# Plot
-	if the.for_value is not None:
+	if the.save_proba:
+		with open('proba.json', 'w') as outfile:
+			json.dump(data, outfile)
+	elif the.for_value is not None:
 		temps = np.linspace(0,temps_max,100)
 		P =  probability(data,temps,the.for_value)
 		plt.plot(temps,P)
@@ -94,4 +99,4 @@ if __name__=="__main__":
 		cbar = fig.colorbar(cs)
 		plt.show()
 
-	remove('run.csv')
+	#remove('run.csv')

snp.py

@@ -86,7 +86,7 @@ if __name__=="__main__":
                         nb_it = the.iters),
                     make.iter(iters.save,
                         filename = the.output_file+".csv",
-                        fmt = "{it} ; {val} ; {sol}\n"),
+                        fmt = "{it} ; {val}\n"),
                     make.iter(iters.log,
                         fmt="\r{it} {val}"),
                     make.iter(iters.history,

try_solver.py

+from os import system
+import json
+import numpy as np
+from scipy.integrate import dblquad
+import matplotlib.pyplot as plt
+
+Nb_run = 5
+
+solvers = [
+    '-m num_greedy -n 45 -i 20 -r 0.08 -t 10000 -w 100 -a 0.01',
+    '-m bit_greedy -n 45 -i 20 -r 0.08 -t 10000 -w 100 -a 0.01'
+]
+
+datas = []
+
+for solver in solvers:
+    print("python3 draw_ert.py --nb-run " + str(Nb_run) + " --save-proba " + solver)
+    system("python3 draw_ert.py --nb-run " + str(Nb_run) + " --save-proba " + solver)
+    with open('proba.json') as json_file:
+        data = json.load(json_file)
+    datas.append(data)
+
+temps_max = 0
+value_max = 0
+value_min = np.inf
+
+for data in datas:
+    for run in data:
+        m = max([int(k) for k,v in run.items()])
+        if m > temps_max:
+            temps_max = m
+        m = max([v for k,v in run.items()])
+        if m > value_max:
+            value_max = m
+        m = min([v for k,v in run.items()])
+        if m < value_min:
+            value_min = m
+
+def probability(data,time,value):
+    num = 0
+    for attempt in data:
+        good = False
+        for t,v in attempt.items():
+            if int(t) < time and v > value:
+                good = True
+                break
+        if good:
+            num += 1
+    return num/len(data)
+
+probability = np.vectorize(probability, excluded=[0])
+
+m = 0
+v = 0
+for i in range(len(solvers)):
+    volume = dblquad(lambda x,y: probability(datas[i],x,y),value_min,value_max,0,temps_max)[0]
+    print(solvers[i])
+    print(volume)
+    if volume > v:
+        m = i
+        v = volume
+
+print("Best solveur:")
+print(solvers[m])
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