Cleanup eigen.py

axelrod/eigen.py

@@ -7,24 +7,24 @@
 
 from typing import Tuple
 
-import numpy
+import numpy as np
 
 
-def _normalise(nvec: numpy.ndarray) -> numpy.ndarray:
+def _normalise(nvec: np.ndarray) -> np.ndarray:
     """Normalises the given numpy array."""
-    with numpy.errstate(invalid="ignore"):
-        result = nvec / numpy.sqrt((nvec @ nvec))
+    with np.errstate(invalid="ignore"):
+        result = nvec / np.sqrt((nvec @ nvec))
     return result
 
 
-def _squared_error(vector_1: numpy.ndarray, vector_2: numpy.ndarray) -> float:
+def _squared_error(vector_1: np.ndarray, vector_2: np.ndarray) -> float:
     """Computes the squared error between two numpy arrays."""
     diff = vector_1 - vector_2
     s = diff @ diff
-    return numpy.sqrt(s)
+    return np.sqrt(s)
 
 
-def _power_iteration(mat: numpy.array, initial: numpy.ndarray) -> numpy.ndarray:
+def _power_iteration(mat: np.array, initial: np.ndarray) -> np.ndarray:
     """
     Generator of successive approximations.
 
@@ -33,7 +33,7 @@ def _power_iteration(mat: numpy.array, initial: numpy.ndarray) -> numpy.ndarray:
     mat: numpy.array
         The matrix to use for multiplication iteration
     initial: numpy.array, None
-        The initial state. Will be set to numpy.array([1, 1, ...]) if None
+        The initial state. Will be set to np.array([1, 1, ...]) if None
 
     Yields
     ------
@@ -42,13 +42,13 @@ def _power_iteration(mat: numpy.array, initial: numpy.ndarray) -> numpy.ndarray:
 
     vec = initial
     while True:
-        vec = _normalise(numpy.dot(mat, vec))
+        vec = _normalise(np.dot(mat, vec))
         yield vec
 
 
 def principal_eigenvector(
-    mat: numpy.array, maximum_iterations=1000, max_error=1e-3
-) -> Tuple[numpy.ndarray, float]:
+    mat: np.array, maximum_iterations=1000, max_error=1e-3
+) -> Tuple[np.ndarray, float]:
     """
     Computes the (normalised) principal eigenvector of the given matrix.
 
@@ -66,12 +66,12 @@ def principal_eigenvector(
     ndarray
         Eigenvector estimate for the input matrix
     float
-        Eigenvalue corresonding to the returned eigenvector
+        Eigenvalue corresponding to the returned eigenvector
     """
 
-    mat_ = numpy.array(mat)
+    mat_ = np.array(mat)
     size = mat_.shape[0]
-    initial = numpy.ones(size)
+    initial = np.ones(size)
 
     # Power iteration
     if not maximum_iterations: