#84 fix dct implementation

spmimage/decomposition/dct.py

@@ -1,32 +1,95 @@
+import itertools
 import numpy as np
 
 
-def generate_dct_dictionary(patch_size: int, sqrt_dict_size: int) -> np.ndarray:
+def zig_zag_index(k: int, n: int):
+    """
+    get k-th index i and j on (n, n)-matrix according to zig-zag scan.
+
+    Parameters:
+    -----------
+        k : int
+            a ranking of element, which we want to know the index i and j
+
+        n : int
+            a size of square matrix
+
+    Returns:
+    -----------
+        (i, j) : Tuple[int, int]
+            the tuple which represents the height and width index of k-th elements
+
+    Reference
+    ----------
+    https://medium.com/100-days-of-algorithms/day-63-zig-zag-51a41127f31
+    """
+    # upper side of interval
+    if k >= n * (n + 1) // 2:
+        i, j = zig_zag_index(n * n - 1 - k, n)
+        return n - 1 - i, n - 1 - j
+
+    # lower side of interval
+    i = int((np.sqrt(1 + 8 * k) - 1) / 2)
+    j = k - i * (i + 1) // 2
+    return (j, i - j) if i & 1 else (i - j, j)
+
+def generate_dct_atom(u, v, n):
+    """
+    generate an (u, v)-th atom of DCT dictionary with size n by n.
+
+    Parameters:
+    -----------
+        u : int
+            an index for height
+
+        v : int
+            an index for width
+
+        n : int
+            a size of DCT
+
+    Returns:
+    -----------
+        atom : np.ndarray
+            (n, n) matrix which represents (u,v)-th atom of DCT dictionary
+    """
+    atom = np.empty((n, n))
+    for i, j in itertools.product(range(n), range(n)):
+        atom[i, j] = np.cos(((i+0.5)*u*np.pi)/n) * np.cos(((j+0.5)*v*np.pi)/n)
+    return atom
+
+def generate_dct_dictionary(n_components: int, patch_size: int) -> np.ndarray:
     """generate_dct_dictionary
     Generate a DCT dictionary.
-    An atom is a (patch_size, patch_size) image, and total number of atoms is 
-    sqrt_dict_size * sqrt_dict_size.
-    The result D is a matrix whose shape is (sqrt_dict_size^2, patch_size^2).
+    An atom is a (patch_size, patch_size) image, and total number of atoms is
+    n_components.
+
+    The result D is a matrix whose shape is (n_components, patch_size ** 2).
     Note that, a row of the result D shows an atom (flatten).
 
     Parameters:
     ------------
-        patch_size : int
-            height and width of an atom of DCT dictionary
+        n_components: int
+            a number of atom, where n_components <= patch_size ** 2.
 
-        sqrt_dict_size : int
-            Total number of DCT atoms is a square number.
-            This parameter fix the number of atoms in the Dictionary.
+        patch_size : int
+            size of atom of DCT dictionary
 
     Returns:
     ------------
-        D : np.ndarray, shape (sqrt_dict_size^2, patch_size^2)
+        D : np.ndarray, shape (n_components, patch_size ** 2)
             DCT dictionary
     """
-    D1 = np.zeros((sqrt_dict_size, patch_size))
-    for k in np.arange(sqrt_dict_size):
-        for i in np.arange(patch_size):
-            D1[k, i] = np.cos(i * k * np.pi / float(sqrt_dict_size))
-        if k != 0:
-            D1[k, :] -= D1[k, :].mean()
-    return np.kron(D1, D1)
+    D = np.empty((n_components, patch_size ** 2))
+
+    if n_components > patch_size ** 2:
+        raise ValueError("n_components must be smaller than patch_size ** 2")
+
+    elif n_components == patch_size ** 2:
+        for i, j in itertools.product(range(patch_size), range(patch_size)):
+            D[i*patch_size + j] = generate_dct_atom(i, j, patch_size).flatten()
+    else:
+        for k in range(n_components):
+            i, j = zig_zag_index(k, patch_size)
+            D[k, :] = generate_dct_atom(i, j, patch_size).flatten()
+    return D