Create utils.py

Author: surajiyer

Diff for: utils.py

@@ -0,0 +1,63 @@
+# coding: utf-8
+
+"""
+    description: Utility functions
+    author: Suraj Iyer
+"""
+
+import numpy as np
+import numba as nb
+
+
+def rowwise_dissimilarity(values):
+    """
+    Compare every row with each other row and count number
+    of differences along column axis per row pairs.
+
+    Example:
+        input: [[1, 2, 3],
+                [1, 3, 1],
+                [2, 2, 2]]
+        output: [[0, 2, 2],
+                 [2, 0, 3]
+                 [2, 3, 0]]
+    """
+    return np.sum(values != values[:, None], axis=-1)
+
+
+def rowwise_cosine_similarity(values):
+    """
+    Using every pair of rows in :values: as input, compute
+    pairwise cosine similarity between each row.
+
+    URL: https://stackoverflow.com/questions/41905029/create-cosine-similarity-matrix-numpy
+    """
+    norm = (values * values).sum(0, keepdims=True) ** .5
+    values = values / norm
+    return (values.T @ values)
+
+
+@nb.njit(parallel=True, fastmath=True)
+def convert_to_ultrametric(values):
+    """
+    Fix triangular inequality within distance matrix (values)
+    by converting to ultra-metric by ensuring the following
+    condition: d_{ij} = min(d_{ij}, max(d_{ik}, d_{kj}))
+
+    Parameters:
+    ------------
+    values: np.ndarray
+        2D square distance matrix.
+
+    Returns:
+    --------
+    np.ndarray
+        Ultrametrified distance matrix.
+    """
+    values = np.atleast_2d(values)
+    result = np.full(values.shape, 1.)
+    for i in nb.prange(values.shape[0]):
+        for j in range(i + 1, values.shape[0]):
+            result[i, j] = result[j, i] = min(np.min(
+                np.fmax(values[i], values[j])), values[i, j])
+    return result