1 files changed, 67 insertions, 0 deletions
diff --git a/src/make_plots.py b/src/make_plots.py
new file mode 100644
index 0000000..9b1fdf4
--- /dev/null
+++ b/src/make_plots.py
@@ -0,0 +1,67 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import cascade_creation
+import convex_optimization
+import algorithms
+import rip_condition
+
+
+def plot_rip_numberofnodes(max_proba, n_min, n_max, p_init, n_cascades, K_max):
+    """
+    Plots the RIP constant for varying number of nodes (n_max included)
+    """
+    x = np.arange(n_min, n_max+1)
+    y = []
+
+    for n_nodes in x:
+        print n_nodes
+        G = cascade_creation.InfluenceGraph(max_proba=.3)
+        G.erdos_init(n=n_nodes, p=.1) #TODO: handle different inits!
+        cascades = cascade_creation.generate_cascades(G, p_init=p_init,
+                                                      n_cascades=n_cascades)
+        M, __ = cascade_creation.icc_matrixvector_for_node(cascades, None)
+        M = cascade_creation.normalize_matrix(M)
+        y.append(rip_condition.find_kth_rip_constants(M, 4)) #
+
+    print y
+
+    plt.clf()
+    plt.plot(x, y)
+    #plt.show()
+
+    return x, y
+
+
+def compare_greedy_and_lagrange_cs284r():
+    """
+    Compares the performance of the greedy algorithm on the
+    lagrangian sparse recovery objective on the Facebook dataset
+    for the CS284r project
+    """
+    G = cascade_creation.InfluenceGraph(max_proba = .8)
+    G.import_from_file("subset_facebook_SNAP.txt")
+    A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=100)
+
+    #Greedy
+    G_hat = algorithms.greedy_prediction(G, A)
+    algorithms.correctness_measure(G, G_hat, print_values=True)
+
+    #Lagrange Objective
+    G_hat = algorithms.recovery_l1obj_l2constraint(G, A,
+            passed_function=convex_optimization.l1obj_l2penalization,
+            floor_cstt=.1, lbda=10)
+    algorithms.correctness_measure(G, G_hat, print_values=True)
+
+
+def test():
+    """
+    unit test
+    """
+    if 0:
+        plot_rip_numberofnodes(max_proba=.3, n_min=30, n_max=30,
+                            p_init=.01, n_cascades=100, K_max=4)
+    if 1:
+        compare_greedy_and_lagrange_cs284r()
+
+if __name__=="__main__":
+    test()