Move real-valued CVPS to cvps.py.

python/cvps.py

@@ -52,6 +52,8 @@ def cvps(sig,c_mad,n_iter,thr):
     a = torch.empty(0)
     b = torch.empty(0)
     c = torch.empty(0)
+    dump = torch.empty(0)
+    ma = torch.empty(0)
     """
 
     # Loop for each client
@@ -95,6 +97,9 @@ def cvps(sig,c_mad,n_iter,thr):
         # Compute the MAD
         mad = 1.4826*c_mad*me
 
+        #dump = torch.cat((dump,ab))
+        #ma = torch.cat((ma,mad.view(1)))
+
         # Avoid division-by-zero
         if mad == 0.0:
             mad = 1e-12
@@ -173,4 +178,94 @@ def cvps(sig,c_mad,n_iter,thr):
     w_ps = torch.square(w_ps)
 
     # Return the projection statistics and median
-    return w_ps,M
+    return w_ps,M#,dump,ma,PS
\ No newline at end of file
+
+# Add white Gaussian noise
+def cvps_real(sig,c_mad,n_iter,thr):
+
+    # Signal power
+    di = sig.shape
+    n_clients = di[1]
+
+    # Allocate the projection statistics output
+    PS = torch.empty((1,n_clients))
+
+    #print(sig.shape)
+    #print(sig[:15,:5])
+
+    # Compute the geometric median
+    M = Weiszfeld(sig,n_iter)
+
+    # Allocate standard projections
+    std_proj = torch.empty(n_clients,dtype=torch.double)
+    sp = torch.zeros(n_clients,n_clients)
+
+    #dump = torch.empty(0)
+    #ma = torch.empty(0)
+
+    # Loop for each client
+    for ux in range(n_clients):
+
+        # Get the data from the client 
+        s = sig[:,ux]
+
+        # Computer the difference to the geometric median
+        diff = s-M
+        no = torch.norm(diff)
+
+        # Avoid division by zero by replacing zeros with a small number
+        eps = torch.tensor(1e-12, dtype=no.dtype, device=no.device)
+        no = torch.where(no == 0, eps, no)
+
+        # Compute a unit vector for each dimension
+        u = diff/no
+
+        # Calculate the standardized projections for each client
+        for nx in range(n_clients):
+
+            # Get the data from the client 
+            s = sig[:,nx]
+
+            # Projections using transpose
+            std_proj[nx] = torch.vdot(s,u)
+
+        # Compute the median of projections for each unit vector
+        m = torch.median(std_proj)
+        
+        # Compute difference between projections and median of projections
+        de = std_proj - m
+
+        # Compute the absolute value of the difference
+        ab = torch.abs(de)
+
+        # Compute the median of the absolute value of the difference
+        me = torch.median(ab)
+
+        # Compute the MAD
+        mad = 1.4826*c_mad*me
+
+        #dump = torch.cat((dump,ab))
+        #ma = torch.cat((ma,mad.view(1)))
+
+        # Avoid division-by-zero
+        if mad == 0.0:
+            mad = 1e-12
+
+        # Compute standardized projections
+        sp[:,ux] = ab/mad
+
+    # Compute the projection statistics, PS, and remove the median
+    PS,_ = torch.max(sp,1)
+    PS = PS - torch.median(PS)
+    PS = torch.where(PS==0, 1e-12, PS)
+
+    # Because we subtracted the median from PS, take abs
+    PS = torch.abs(PS)
+
+    # Compute the PS weights
+    w_ps = thr/PS
+    w_ps = torch.where(w_ps>1, 1.0, w_ps)
+    w_ps = torch.square(w_ps)
+
+    # Return the projection statistics and median
+    return w_ps,M#,dump,ma,PS
\ No newline at end of file

python/cvps_real.py

-#!/usr/bin/python3  
-
-import numpy as np
-import torch
-from Weiszfeld import Weiszfeld
-from Weiszfeld import ComplexWeiszfeld
-import statistics
-
-# Add white Gaussian noise
-def cvps_real(sig,c_mad,n_iter,thr):
-
-    # Signal power
-    di = sig.shape
-    #p_neurons = di[0]
-    n_clients = di[1]
-
-    # Allocate the projection statistics output
-    PS = torch.empty((1,n_clients));
-
-    # Compute the geometric median
-    M = Weiszfeld(sig,n_iter)
-
-    # Allocate standard projections
-    std_proj = torch.empty(n_clients,dtype=torch.double)
-    sp = torch.zeros(n_clients,n_clients)
-
-    #dump = torch.empty(0)
-    #ma = 0
-
-    # Loop for each client
-    for ux in range(0,n_clients):
-
-        # Get the data from the client 
-        s = sig[:,ux]
-
-        # Computer the difference to the geometric median
-        diff = s-M
-        no = torch.norm(diff)
-
-        # Avoid division by zero by replacing zeros with a small number
-        eps = torch.tensor(1e-12, dtype=no.dtype, device=no.device)
-        no = torch.where(no == 0, eps, no)
-
-        # Compute a unit vector for each dimension
-        u = diff/no
-
-        # Calculate the standardized projections for each client
-        for nx in range(n_clients):
-
-            # Get the data from the client 
-            s = sig[:,nx]
-
-            # Projections using transpose
-            std_proj[nx] = torch.vdot(s,u)
-
-        # Compute the median of projections for each unit vector
-        m = torch.median(std_proj)
-        
-        # Compute difference between projections and median of projections
-        de = std_proj - m
-
-        # Compute the absolute value of the difference
-        ab = torch.abs(de)
-
-        #dump = torch.cat((dump,ab))
-
-        # Compute the median of the absolute value of the difference
-        me = torch.median(ab)
-
-        # Compute the MAD
-        mad = 1.4826*c_mad*me
-        #ma = ma + mad
-
-        # Avoid division-by-zero
-        if mad == 0.0:
-            mad = 1e-12
-
-        # Compute standardized projections
-        sp[:,ux] = ab/mad
-
-    # Compute the projection statistics, PS, and remove the median
-    PS,_ = torch.max(sp,1)
-    PS = PS - torch.median(PS)
-
-    if PS.isnan().any():
-        print(PS)
-
-    # Because we subtracted the median from PS, take abs
-    PS = torch.abs(PS)
-
-    # Compute the PS weights
-    w_ps = thr/PS
-    w_ps = torch.where(w_ps>1, 1.0, w_ps)
-    w_ps = torch.square(w_ps)
-
-    # Return the projection statistics and median
-    return w_ps, M
\ No newline at end of file