adding four figures

2 files changed, 57 insertions, 5 deletions

diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index 8dc6f82..0d506e1 100644
--- a/src/convex_optimization.py
+++ b/src/convex_optimization.py
@@ -90,7 +90,7 @@ def diff_and_opt(M_val, w_val, f_x, f_xz):
 
     def F(x=None, z=None):
         if x is None:
-            return 0, cvxopt.matrix(-.001, (n,1))
+            return 0, cvxopt.matrix(-.1, (n,1))
         elif z is None:
             y, y_diff = f_x(x, M_val, w_val)
             return cvxopt.matrix(float(y), (1, 1)),\
diff --git a/src/make_plots.py b/src/make_plots.py
index d83eb2c..792e325 100644
--- a/src/make_plots.py
+++ b/src/make_plots.py
@@ -108,6 +108,58 @@ def plot_barabasi_albert_graph():
 
 
 
+def plot_kronecker_l2norm():
+    plt.clf()
+    fig = plt.figure(1)
+    x = [50, 100, 500, 1000, 2000]
+    sparse_recov = [62, 60, 36, 28, 21]
+    max_likel = [139, 101, 42, 31, 25]
+    lasso = [50, 48, 33, 29, 23]
+
+    fig, ax = plt.subplots()
+
+    plt.subplots_adjust(bottom=.2, top=.85)
+    plt.xticks(ha="right", rotation=45)
+
+    plt.axis((50, 2000, 0, 145))
+    plt.xlabel("Number of Cascades")
+    plt.ylabel("l2-norm")
+    plt.grid(color="lightgrey")
+    ax.plot(x, lasso, 'ko-', color="orange", label="Lasso")
+    ax.plot(x, max_likel, 'ko-', color="cornflowerblue", label="MLE")
+    ax.plot(x, sparse_recov, 'ko-', color="k", label="Our Method")
+    plt.legend(loc="upper right")
+    ax.set_xticks(x)
+    ax.set_xticklabels(tuple(x))
+    plt.savefig("../paper/figures/"+"kronecker_l2_norm.pdf")
+
+
+def plot_kronecker_l2norm_nonsparse():
+    plt.clf()
+    fig = plt.figure(1)
+    x = [50, 100, 500, 1000, 2000]
+    sparse_recov = [56, 55, 28, 21, 15]
+    max_likel = [125, 80, 35, 25, 20]
+    lasso = [47, 47, 27, 22, 17]
+
+    fig, ax = plt.subplots()
+
+    plt.subplots_adjust(bottom=.2, top=.85)
+    plt.xticks(ha="right", rotation=45)
+
+    plt.axis((50, 2000, 0, 145))
+    plt.xlabel("Number of Cascades")
+    plt.ylabel("l2-norm")
+    plt.grid(color="lightgrey")
+    ax.plot(x, lasso, 'ko-', color="orange", label="Lasso")
+    ax.plot(x, max_likel, 'ko-', color="cornflowerblue", label="MLE")
+    ax.plot(x, sparse_recov, 'ko-', color="k", label="Our Method")
+    plt.legend(loc="upper right")
+    ax.set_xticks(x)
+    ax.set_xticklabels(tuple(x))
+    plt.savefig("../paper/figures/"+"kronecker_l2_norm_nonsparse.pdf")
+
+
 def plot_ROC_curve(figure_name):
     """
     plot information in a pretty way
@@ -168,8 +220,8 @@ if __name__=="__main__":
             #convex_optimization.type_lasso)
     if 1:
         compute_graph("../datasets/kronecker_graph_256_cross.txt", 
-            n_cascades=2000, lbda=0.1, min_proba=.2, max_proba=.7,
+            n_cascades=50, lbda=0., min_proba=.2, max_proba=.7,
             passed_function=
-            #convex_optimization.sparse_recovery, 
-            convex_optimization.type_lasso,
-            sparse_edges=False)
\ No newline at end of file
+            convex_optimization.sparse_recovery, 
+            #convex_optimization.type_lasso,
+            sparse_edges=True)
\ No newline at end of file