Merge pull request #23 from Ceow/master

Little fixes

.gitignore

@@ -102,3 +102,6 @@ ENV/
 
 # pytest
 .pytest_cache/
+
+# PyCharm
+pylocus/.idea/

pylocus/algorithms.py

@@ -46,9 +46,10 @@ def execute_method(method, measured_matrix=None, all_points=None, W=None, **kwar
         xhat, costs = reconstruct_dwmds(measured_matrix, W=W, X0=X0, **kwargs)
     if method == 'SRLS':
         n = kwargs.get('n', 1)
+        z = kwargs.get('z', None)
         rescale = kwargs.get('rescale', False)
         xhat = reconstruct_srls(measured_matrix, all_points,
-                                n=n, W=W, rescale=rescale)
+                                n=n, W=W, rescale=rescale, z=z)
     return xhat
 
 

pylocus/edm_completion.py

@@ -111,11 +111,11 @@ def kappa_cvx(gram, n):
 
     if method == 'maximize':
         obj = Maximize(trace(H) - lamda *
-                       norm(multiply(W, (edm_optimize - edm_missing))))
+                       norm(multiply(W, (edm_optimize - edm_missing)), p=1))
     # TODO: add a reference to paper where "minimize" is used instead of maximize. 
     elif method == 'minimize':
         obj = Minimize(trace(H) + lamda *
-                       norm(multiply(W, (edm_optimize - edm_missing))))
+                       norm(multiply(W, (edm_optimize - edm_missing)), p=1))
 
     prob = Problem(obj)
 
@@ -136,7 +136,7 @@ def kappa_cvx(gram, n):
         if H.value is not None:
             print('trace of H:', np.trace(H.value))
         print('other cost:', lamda *
-              norm(multiply(W, (edm_complete - edm_missing))).value)
+              norm(multiply(W, (edm_complete - edm_missing)), p=1).value)
 
     return np.array(edm_complete)
 

pylocus/lateration.py

@@ -99,9 +99,12 @@ def phi_prime(_lambda):
     if rescale and n < d + 2:
         raise ValueError(
             'A minimum of d + 2 ranges are necessary for rescaled ranging.')
-    elif n < d + 1:
+    elif n < d + 1 and z is None:
         raise ValueError(
             'A minimum of d + 1 ranges are necessary for ranging.')
+    elif n < d:
+        raise ValueError(
+            'A minimum of d ranges are necessary for ranging.')
 
     Sigma = np.diagflat(np.power(w, 0.5))