changed the visualization

demos/MC_ALS.py

 import SetProxPythonPath
 from proxtoolbox.experiments.MC.MC_Experiment import MC_Experiment
 
-MC = MC_Experiment(algorithm='ALS', fraction_known_entrees=0.5, rank=2, anim=True)
+MC = MC_Experiment(algorithm='ALS', fraction_known_entrees=0.3, rank=6, anim=True, n_2=200)
 MC.run()
 MC.animate(MC.algorithm)
 MC.show()
\ No newline at end of file

demos/MC_AP.py

 import SetProxPythonPath
 from proxtoolbox.experiments.MC.MC_Experiment import MC_Experiment
 
-MC = MC_Experiment(algorithm='AP', fraction_known_entrees=0.4, rank=2, anim=True)
+MC = MC_Experiment(algorithm='AP', fraction_known_entrees=0.4, rank=6, anim=True, n_2=200)
 MC.run()
 MC.animate(MC.algorithm)
 MC.show()
\ No newline at end of file

demos/MC_DR.py

 import SetProxPythonPath
 from proxtoolbox.experiments.MC.MC_Experiment import MC_Experiment
 
-MC = MC_Experiment(algorithm='DR', fraction_known_entrees=0.5, anim=True)
+MC = MC_Experiment(algorithm='DR', fraction_known_entrees=0.3, anim=True, n_2=200)
 MC.run()
 MC.animate(MC.algorithm)
 MC.show()
\ No newline at end of file

proxtoolbox/experiments/MC/MC_Experiment.py

@@ -48,14 +48,30 @@ class MC_Experiment(Experiment):
         self.diagnostic = False
         self.gamma = gamma
         self.lambd = lambd
+        self.frob_list = []
+        self.rank_list = []
 
     def loadData(self):
         self.mask = np.random.rand(self.n_1, self.n_2) < self.p
         # self.u0 = (np.random.randn(self.n_1, self.r))@(np.random.randn(self.r, self.n_2))
         
         self.u0 = np.ones((self.n_1, self.n_2))
-        self.u0[10:70, 10:20] = 0
-        self.u0[10:70, 50:60] = 0
+        self.u0[20:80, 10:20] = 0
+        self.u0[30:40, 20:30] = 0
+        self.u0[40:60, 30:40] = 0
+        self.u0[60:70, 40:50] = 0
+        self.u0[20:80, 50:60] = 0
+
+        self.u0[40:80, 80:90] = 0.5
+        self.u0[20:40, 90:100] = 0.5
+        self.u0[60:70, 90:100] = 0.5
+        self.u0[40:80, 100:110] = 0.5
+
+        self.u0[20:80, 130:140] = 0
+        self.u0[20:30, 140:150] = 0
+        self.u0[20:60, 150:160] = 0
+        self.u0[20:30, 160:170] = 0
+        self.u0[20:80, 170:180] = 0
         
         self.M = np.copy(self.u0)
         if self.algorithm_name == 'ALS':
@@ -81,24 +97,34 @@ class MC_Experiment(Experiment):
         # for key in self.output['stats']:
         #     print(key)
 
-        fig, ax = plt.subplots(1, 3, figsize=(16, 5))
+        fig, ((ax11, ax12), (ax21, ax22)) = plt.subplots(2, 2, figsize=(16, 5))
 
-        ax[0].imshow(self.M)
-        ax[0].set(title = 'original matrix')
-        ax[1].imshow(u)
-        ax[1].set(title = 'reconstructed matrix')
-        ax[2].semilogy(ch)
-        ax[2].set(xlabel='$k$', ylabel='$|| X_{k-1} - X_k ||$', title='change for each iteration')
+        ax11.imshow(self.M)
+        ax11.set(title = 'original matrix')
+        ax12.imshow(self.M * self.mask)
+        ax12.set(title = 'algorithm input')
+        ax21.semilogy(self.frob_list)
+        ax21.set(xlabel='$k$', ylabel='$|| M - X_k ||$', title='distance to M')
+        ax22.semilogy(ch)
+        ax22.set(xlabel='$k$', ylabel='$|| X_{k-1} - X_k ||$', title='change for each iteration')
         plt.show()
 
     def animate(self, alg):
-        # print(alg.u_new)
-        # print(alg.u_new)
         if alg.u_new is not None:
             if self.algorithm_name == 'DR':
                 self.animateFigure(alg.proxOperators[0].eval(alg.u_new), f'$X_{{{alg.iter}}}$')
+                self.frob_list += [np.linalg.norm(alg.proxOperators[0].eval(alg.u_new) - self.M)]
+                self.rank_list += [np.linalg.matrix_rank(alg.u_new)]
             else:
                 self.animateFigure(alg.u_new, f'$X_{{{alg.iter}}}$')
+                self.frob_list += [np.linalg.norm(alg.u_new - self.M)]
+                self.rank_list += [np.linalg.matrix_rank(alg.u_new)]
             if alg.iter < 50:
                 time.sleep(0.3)
-            time.sleep(0.1)
\ No newline at end of file
+            time.sleep(0.1)
+        else:
+            if self.algorithm_name == 'ALS':
+                self.animateFigure(self.u0[0].T@self.u0[1], '$X_0$')
+            else:
+                self.animateFigure(self.u0, '$X_0$')
+            time.sleep(0.7)
\ No newline at end of file

proxtoolbox/proxoperators/MC_prox_nuc.py

@@ -11,8 +11,11 @@ class MC_prox_nuc(ProxOperator):
         self.gamma = experiment.gamma
 
     def eval(self, u):
+        n_1, n_2 = u.shape
         U, sigma, V = np.linalg.svd(u)
         sigma = sigma - self.gamma
         sigma[sigma < 0] = 0
-        E = np.diag(sigma)
+        n = min(n_1, n_2)
+        E = np.zeros((n_1, n_2))
+        E[:n, :n] = np.diag(sigma)
         return U @ E @ V
\ No newline at end of file