Fixed matrix construction logic to create row vectors from 1D lists

main.py

@@ -123,8 +123,8 @@
 
 # TEST GAUSS-NEWTON ITERATION
 h = Matrix([lambda a,x: pow(a,x)])
-independents = Matrix([1, 2, 3])
-y = Matrix([2, 4, 8])
+independents = Matrix([1, 2, 3]).get_transpose()
+y = Matrix([2, 4, 8]).get_transpose()
 # let J be the Jacobian of h(x)
 J = Matrix([lambda a,x: x*pow(a,x-1)])
 
@@ -141,3 +141,8 @@
 print(centers)
 assert(utils.equals_approx(centers,Matrix([[-0.6667, 1.0],[20.75, 0.0]]),precision = 1e-4))
 
+
+# TEST SUBTRACTIVE CLUSTERING
+density_points = Matrix([[1,2], [0.5,1.5], [0,1], [0,0.5], [0,0], [0,-0.5], [0,-1], [0.5,-1.5], [1,-2], [2,0], [2.5,-0.5], [3,-1], [3,-1.5], [3,-2], [3,-2.5], [3,-3], [2.5,-3.5], [2,-4]])
+mountaintops = clustering.find_density_clusters(data=density_points, num_centers=2, beta = 0.25, sigma = 0.25)
+print(mountaintops)

mola/clustering.py

@@ -1,5 +1,3 @@
-from asyncio.windows_events import INFINITE
-from json.encoder import INFINITY
 from mola.matrix import Matrix
 from mola.utils import zeros, get_mean, uniques, randoms, norm
 from random import random
@@ -107,7 +105,7 @@ def find_k_means(data: Matrix, num_centers = 2, max_iterations = 100, distance_f
                 if closest_center[row] == i:
                     points_in_cluster.append(data.get_row(row))
             if len(points_in_cluster) > 0:
-                centers[i,:] = get_mean(points_in_cluster).get_transpose()
+                centers[i,:] = get_mean(points_in_cluster)
 
         # if the centers remained the same as in previous iteration, break out of the loop
         if centers == previous_centers:
@@ -260,7 +258,7 @@ def find_density_clusters(data: Matrix, num_centers = 2, beta = 0.5, sigma = 0.5
 
 
         # save cluster centers
-        c_subtractive[k,:] = data[peak_i,:].get_transpose()
+        c_subtractive[k,:] = data[peak_i,:]
 
         # destruct mountain functions
         for i in range(n_samples):

mola/decomposition.py

@@ -202,7 +202,7 @@ def power_method(A: Matrix) -> Matrix:
     """
 
     # initialize the vector with random values using list comprehension
-    vector = Matrix([random() for x in range(A.get_height())])
+    vector = Matrix([random() for x in range(A.get_height())]).get_transpose()
 
     # do the iteration to (hopefully) converge the vector towards its dominant eigenvector
     for i in range(100):

mola/matrix.py

@@ -70,15 +70,11 @@ def __construct_from_lists(self,lists):
                 row = lists[j]
                 col.append(row)
             self.data = col
-        # if the input parameter was not a nested list, assume it was a simple 1D list and construct a matrix with a single column
+        # if the input parameter was not a nested list, assume it was a simple 1D list and construct a Matrix that is actually a row vector
         elif isinstance(lists,list):
-            self.n_rows = len(lists)
-            self.n_cols = 1
-            col = []
-            for j in range(self.n_rows):
-                row = lists[j]
-                col.append([row])
-            self.data = col        
+            self.n_rows = 1
+            self.n_cols = len(lists)
+            self.data = [lists]
 
     def __abs__(self):
         """
@@ -123,7 +119,7 @@ def __getitem__(self,idx):
             # rows is slice and cols is int (several values from a single column are queried)
             elif isinstance(rows,slice) and isinstance(cols,int):
                 #sliced_data = Matrix([r[cols] for r in self.data[rows]])
-                return Matrix([r[cols] for r in self.data[rows]])
+                return Matrix([r[cols] for r in self.data[rows]]).get_transpose()
             # if both are slices (a submatrix is queried)
             elif isinstance(rows,slice) and isinstance(cols,slice):
                 #newlist = [r[cols] for r in self.data[rows]]
@@ -327,7 +323,7 @@ def __sub__(self,other):
         output = Matrix(self.n_rows,self.n_cols,0)
         # first, ensure that the dimensions of the matrices match
         if self.n_rows != other.n_rows or self.n_cols != other.n_cols:
-            raise Exception("Matrix dimensions must match for elementwise addition or subtraction!")
+            raise Exception("Matrix dimensions must match for elementwise addition or subtraction! Left side is " + str(self.n_rows) + "x" + str(self.n_cols) + ", right side is " + str(other.n_rows) + "x" + str(other.n_cols))
         # if we passed the dimension check, we can subtract the matrices elementwise
         for i in range(self.n_rows):
             for j in range(self.n_cols):