From a603cb5864f71a4a84fa5178d4758bc3b1785bdb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jure=20=C5=A0orn?= Date: Mon, 16 Dec 2024 03:52:32 +0100 Subject: [PATCH] Closure, NumPy --- README.md | 57 +++++++++++++++++++++++++-------------------------- index.html | 60 ++++++++++++++++++++++++++++-------------------------- 2 files changed, 59 insertions(+), 58 deletions(-) diff --git a/README.md b/README.md index cfa0956..8592784 100644 --- a/README.md +++ b/README.md @@ -840,7 +840,7 @@ import . # Imports a built-in or '/.py'. Closure ------- -**We have/get a closure in Python when a nested function references a value of its enclosing function and then the enclosing function returns the nested function.** +**We have/get a closure in Python when a nested function references a value of its enclosing function and then the enclosing function returns its nested function.** ```python def get_multiplier(a): @@ -2717,50 +2717,49 @@ import numpy as np ### Broadcasting **A set of rules by which NumPy functions operate on arrays of different shapes.** - ```python -left = [ 0.1 , 0.6 , 0.8 ] # Shape: (3,) -right = [[0.1], [0.6], [0.8]] # Shape: (3, 1) +left = np.array([ 0.1, 0.6, 0.8 ]) # `left.shape == (3,)` +right = np.array([[0.1],[0.6],[0.8]]) # `right.shape == (3, 1)` ``` #### 1. If array shapes differ in length, left-pad the shorter shape with ones: ```python -left = [[0.1 , 0.6 , 0.8]] # Shape: (1, 3) <- ! -right = [[0.1], [0.6], [0.8]] # Shape: (3, 1) +left = np.array([[0.1, 0.6, 0.8]]) # `left.shape == (1, 3)` +right = np.array([[0.1],[0.6],[0.8]]) # `right.shape == (3, 1)` ``` #### 2. If any dimensions differ in size, expand the ones that have size 1 by duplicating their elements: ```python -left = [[0.1, 0.6, 0.8], # Shape: (3, 3) <- ! - [0.1, 0.6, 0.8], - [0.1, 0.6, 0.8]] -``` +left = np.array([[0.1, 0.6, 0.8], # `left.shape == (3, 3)` + [0.1, 0.6, 0.8], + [0.1, 0.6, 0.8]]) -```python -right = [[0.1, 0.1, 0.1], # Shape: (3, 3) <- ! - [0.6, 0.6, 0.6], - [0.8, 0.8, 0.8]] +right = np.array([[0.1, 0.1, 0.1], # `right.shape == (3, 3)` + [0.6, 0.6, 0.6], + [0.8, 0.8, 0.8]]) ``` ### Example #### For each point returns index of its nearest point (`[0.1, 0.6, 0.8] => [1, 2, 1]`): ```python ->>> points = np.array([0.1, 0.6, 0.8]) -[ 0.1, 0.6, 0.8 ] ->>> wrapped_points = points.reshape(3, 1) -[[0.1], [0.6], [0.8]] ->>> deltas = points - wrapped_points -[[ 0. , 0.5, 0.7], - [-0.5, 0. , 0.2], - [-0.7, -0.2, 0. ]] ->>> distances = np.abs(deltas) ->>> distances[range(3), range(3)] = np.inf -[[ inf, 0.5, 0.7], - [ 0.5, inf, 0.2], - [ 0.7, 0.2, inf]] ->>> distances.argmin(1) -[1, 2, 1] +>>> print(points := np.array([0.1, 0.6, 0.8])) +[0.1 0.6 0.8] +>>> print(wrapped_points := points.reshape(3, 1)) +[[0.1] + [0.6] + [0.8]] +>>> print(deltas := points - wrapped_points) +[[ 0. 0.5 0.7] + [-0.5 0. 0.2] + [-0.7 -0.2 0. ]] +>>> deltas[range(3), range(3)] = np.inf +>>> print(distances := np.abs(deltas)) +[[inf 0.5 0.7] + [0.5 inf 0.2] + [0.7 0.2 inf]] +>>> print(distances.argmin(axis=1)) +[1 2 1] ``` diff --git a/index.html b/index.html index 9e97a0a..00d14fb 100644 --- a/index.html +++ b/index.html @@ -55,7 +55,7 @@
- +
@@ -701,7 +701,7 @@ player = Player(point, direction) #
  • Directory of the file that is passed to python command serves as a root of local imports.
  • For relative imports use 'from .[…][<pkg/module>[.…]] import <obj>'.
    • -

    #Closure

    We have/get a closure in Python when a nested function references a value of its enclosing function and then the enclosing function returns the nested function.

    def get_multiplier(a):
+
    #Closure
    We have/get a closure in Python when a nested function references a value of its enclosing function and then the enclosing function returns its nested function.
    def get_multiplier(a):
     def out(b):
         return a * b
     return out
@@ -2216,39 +2216,41 @@ $ snakeviz test.prof                                            'ix_([1, 2], [3, 4])' returns '[[1], [2]]' and '[[3, 4]]'. Due to broadcasting rules, this is the same as using '[[1, 1], [2, 2]]' and '[[3, 4], [3, 4]]'.
 
  • Any value that is broadcastable to the indexed shape can be assigned to the selection.
    • 
 
-
    Broadcasting
    A set of rules by which NumPy functions operate on arrays of different shapes.
    left  = [ 0.1 ,  0.6 ,  0.8 ]                           # Shape: (3,)
-right = [[0.1], [0.6], [0.8]]                           # Shape: (3, 1)
+
    Broadcasting
    A set of rules by which NumPy functions operate on arrays of different shapes.
    left  = np.array([ 0.1,  0.6,  0.8 ])                   # `left.shape  == (3,)`
+right = np.array([[0.1],[0.6],[0.8]])                   # `right.shape == (3, 1)`
 
    
 
 
-
    1. If array shapes differ in length, left-pad the shorter shape with ones:
    left  = [[0.1 ,  0.6 ,  0.8]]                           # Shape: (1, 3) <- !
-right = [[0.1], [0.6], [0.8]]                           # Shape: (3, 1)
+
    1. If array shapes differ in length, left-pad the shorter shape with ones:
    left  = np.array([[0.1,  0.6,  0.8]])                   # `left.shape  == (1, 3)`
+right = np.array([[0.1],[0.6],[0.8]])                   # `right.shape == (3, 1)`
 
    
 
-
    2. If any dimensions differ in size, expand the ones that have size 1 by duplicating their elements:
    left  = [[0.1,  0.6,  0.8],                             # Shape: (3, 3) <- !
-         [0.1,  0.6,  0.8],
-         [0.1,  0.6,  0.8]]
+
    2. If any dimensions differ in size, expand the ones that have size 1 by duplicating their elements:
    left  = np.array([[0.1,  0.6,  0.8],                    # `left.shape  == (3, 3)`
+                  [0.1,  0.6,  0.8],
+                  [0.1,  0.6,  0.8]])
+
+right = np.array([[0.1,  0.1,  0.1],                    # `right.shape == (3, 3)`
+                  [0.6,  0.6,  0.6],
+                  [0.8,  0.8,  0.8]])
 
    
 
-
    right = [[0.1,  0.1,  0.1],                             # Shape: (3, 3) <- !
-         [0.6,  0.6,  0.6],
-         [0.8,  0.8,  0.8]]
-
    
-
    Example
    For each point returns index of its nearest point ([0.1, 0.6, 0.8] => [1, 2, 1]):
    >>> points = np.array([0.1, 0.6, 0.8])
-[ 0.1,  0.6,  0.8 ]
->>> wrapped_points = points.reshape(3, 1)
-[[0.1], [0.6], [0.8]]
->>> deltas = points - wrapped_points
-[[ 0. ,  0.5,  0.7],
- [-0.5,  0. ,  0.2],
- [-0.7, -0.2,  0. ]]
->>> distances = np.abs(deltas)
->>> distances[range(3), range(3)] = np.inf
-[[ inf,  0.5,  0.7],
- [ 0.5,  inf,  0.2],
- [ 0.7,  0.2,  inf]]
->>> distances.argmin(1)
-[1, 2, 1]
+
    Example
    For each point returns index of its nearest point ([0.1, 0.6, 0.8] => [1, 2, 1]):
    >>> print(points := np.array([0.1, 0.6, 0.8]))
+[0.1  0.6  0.8]
+>>> print(wrapped_points := points.reshape(3, 1))
+[[0.1]
+ [0.6]
+ [0.8]]
+>>> print(deltas := points - wrapped_points)
+[[ 0.   0.5  0.7]
+ [-0.5  0.   0.2]
+ [-0.7 -0.2  0. ]]
+>>> deltas[range(3), range(3)] = np.inf
+>>> print(distances := np.abs(deltas))
+[[inf  0.5  0.7]
+ [0.5  inf  0.2]
+ [0.7  0.2  inf]]
+>>> print(distances.argmin(axis=1))
+[1 2 1]
 
    
 
 
@@ -2922,7 +2924,7 @@ $ deactivate                # Deactivates the active