Update generated files

100_Numpy_exercises_with_hints_with_solutions.md

@@ -202,7 +202,7 @@ color = np.dtype([("r", np.ubyte),
 `hint:`
 
 ```python
-Z = np.dot(np.ones((5,3)), np.ones((3,2)))
+Z = np.matmul(np.ones((5, 3)), np.ones((3, 2)))
 print(Z)
 
 # Alternative solution, in Python 3.5 and above
@@ -894,6 +894,32 @@ P0 = np.random.uniform(-10, 10, (10,2))
 P1 = np.random.uniform(-10,10,(10,2))
 p = np.random.uniform(-10, 10, (10,2))
 print(np.array([distance(P0,P1,p_i) for p_i in p]))
+
+# Author: Yang Wu (Broadcasting)
+def distance_points_to_lines(p: np.ndarray, p_1: np.ndarray, p_2: np.ndarray) -> np.ndarray:
+    x_0, y_0 = p.T  # Shape -> (n points, )
+    x_1, y_1 = p_1.T  # Shape -> (n lines, )
+    x_2, y_2 = p_2.T  # Shape -> (n lines, )
+
+    # Displacement vector coordinates from p_1 -> p_2
+    dx = x_2 - x_1  # Shape -> (n lines, )
+    dy = y_2 - y_1  # Shape -> (n lines, )
+
+    # The 'cross product' term
+    cross_term = x_2 * y_1 - y_2 * x_1  # Shape -> (n lines, )
+
+    # Broadcast x_0, y_0 (n points, 1) and dx, dy, cross_term (1, n lines) -> (n points, n lines)
+    numerator = np.abs(
+        dy[np.newaxis, :] * x_0[:, np.newaxis]
+        - dx[np.newaxis, :] * y_0[:, np.newaxis]
+        + cross_term[np.newaxis, :]
+    )
+    denominator = np.sqrt(dx**2 + dy**2)  # Shape -> (n lines, )
+
+    # Shape (n points, n lines) / (1, n_lines) -> (n points, n lines)
+    return numerator / denominator[np.newaxis, :]
+
+distance_points_to_lines(p, P0, P1)
 ```
 #### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)
 `hint: minimum maximum`

100_Numpy_exercises_with_solutions.md

@@ -202,7 +202,7 @@ color = np.dtype([("r", np.ubyte),
 
 
 ```python
-Z = np.dot(np.ones((5,3)), np.ones((3,2)))
+Z = np.matmul(np.ones((5, 3)), np.ones((3, 2)))
 print(Z)
 
 # Alternative solution, in Python 3.5 and above
@@ -894,6 +894,32 @@ P0 = np.random.uniform(-10, 10, (10,2))
 P1 = np.random.uniform(-10,10,(10,2))
 p = np.random.uniform(-10, 10, (10,2))
 print(np.array([distance(P0,P1,p_i) for p_i in p]))
+
+# Author: Yang Wu (Broadcasting)
+def distance_points_to_lines(p: np.ndarray, p_1: np.ndarray, p_2: np.ndarray) -> np.ndarray:
+    x_0, y_0 = p.T  # Shape -> (n points, )
+    x_1, y_1 = p_1.T  # Shape -> (n lines, )
+    x_2, y_2 = p_2.T  # Shape -> (n lines, )
+
+    # Displacement vector coordinates from p_1 -> p_2
+    dx = x_2 - x_1  # Shape -> (n lines, )
+    dy = y_2 - y_1  # Shape -> (n lines, )
+
+    # The 'cross product' term
+    cross_term = x_2 * y_1 - y_2 * x_1  # Shape -> (n lines, )
+
+    # Broadcast x_0, y_0 (n points, 1) and dx, dy, cross_term (1, n lines) -> (n points, n lines)
+    numerator = np.abs(
+        dy[np.newaxis, :] * x_0[:, np.newaxis]
+        - dx[np.newaxis, :] * y_0[:, np.newaxis]
+        + cross_term[np.newaxis, :]
+    )
+    denominator = np.sqrt(dx**2 + dy**2)  # Shape -> (n lines, )
+
+    # Shape (n points, n lines) / (1, n_lines) -> (n points, n lines)
+    return numerator / denominator[np.newaxis, :]
+
+distance_points_to_lines(p, P0, P1)
 ```
 #### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)
 

100_Numpy_random.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e06c1964",
+   "id": "ed5b154a",
    "metadata": {},
    "source": [
     "# 100 numpy exercises\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c108c1c4",
+   "id": "f21bc71d",
    "metadata": {},
    "source": [
     "File automatically generated. See the documentation to update questions/answers/hints programmatically."
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2aefe09b",
+   "id": "7a3d8374",
    "metadata": {},
    "source": [
     "Run the `initialize.py` module, then call a random question with `pick()` an hint towards its solution with\n",
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "55c8f855",
+   "id": "a3aaf46b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8f1e8a4a",
+   "id": "6db78cf9",
    "metadata": {},
    "outputs": [],
    "source": [