Worked on synthetic data recovery so we can tell how high the actual

infector is ranked among all potential parents. Cleaned up code for the
predicting victims benchmarking test.

1 files changed, 16 insertions, 16 deletions

diff --git a/experiments/process.py b/experiments/process.py
index 5f3cff4..f31e0d4 100644
--- a/experiments/process.py
+++ b/experiments/process.py
@@ -15,20 +15,20 @@ if __name__ == "__main__":
         sys.exit("usage: {0} <file>".format(sys.argv[0]))
     root_victims, victims, non_victims, age = load(open(sys.argv[1]))
     
-    # # alphas = np.arange(1e-3, 1e-2, 8e-4)  # parameter of the time component
-    # alphas = np.logspace(-3,0,num=15)
-    # # deltas = np.arange(0.001, 0.3, 0.008)  # parameter of the structural component
-    # deltas = np.logspace(-4,-1.7,num=15)
-    # with open("out.log", "w") as fh:
-    #     for alpha, delta in product(alphas, deltas):
-    #         beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta)
-    #         print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
-    #         fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n")
-    #         fh.flush()
+    # alphas = np.arange(1e-3, 1e-2, 8e-4)  # parameter of the time component
+    alphas = np.logspace(-4,-1,num=15)
+    # deltas = np.arange(0.001, 0.3, 0.008)  # parameter of the structural component
+    deltas = np.logspace(-4,-1.7,num=15)
+    with open("out.log", "w") as fh:
+        for alpha, delta in product(alphas, deltas):
+            beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta)
+            print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
+            fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n")
+            fh.flush()
 
-    # alphas = np.arange(.03, .065, .001)  # parameter of the time component
+    # alphas = np.arange(.04, .075, .003)  # parameter of the time component
     # # alphas = np.logspace(-4,-1,num=20)
-    # deltas = np.arange(.09, .11, .001)  # parameter of the structural component
+    # deltas = np.arange(.07, .11, .003)  # parameter of the structural component
     # # deltas = np.logspace(-2,-.5,num=20)
     # lmbda = 0.10#np.logspace(-12,-3,num=10)
     # with open("out.log", "w") as fh:
@@ -38,7 +38,7 @@ if __name__ == "__main__":
     #         fh.write("\t".join(map(str, [alpha, delta, lmbda, roots, ll])) + "\n")
     #         fh.flush()
 
-    alpha = 0.041
-    delta = 0.01
-    beta, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, 1.)
-    print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
\ No newline at end of file
+    # alpha = 0.061
+    # delta = 0.082
+    # beta, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, 1.)
+    # print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
\ No newline at end of file