q78 explained

Author: Hemanth21k

source/exercises100.ktx

@@ -1094,7 +1094,7 @@ P0 = np.random.uniform(-10,10,(10,2))
 P1 = np.random.uniform(-10,10,(10,2))
 p  = np.random.uniform(-10,10,( 1,2))
 
-def distance(P0,P1,p):
+def distance_faster(P0,P1,p):
     '''
     Author: Hemanth Pasupuleti
     Reference: https://mathworld.wolfram.com/Point-LineDistance2-Dimensional.html
@@ -1111,17 +1111,17 @@ def distance(P0,P1,p):
 
     d = np.abs(np.einsum("ij,ij->i",r,v)) / norm # Shape: (n_lines,), Compute: d = |(x1-x0)*(y2-y1)-(y1-y0)*(x1-x0)|/sqrt((x2-x1)**2 + (y2-y1)**2)
     return d
-print(distance(P0, P1, p))
+print(distance_faster(P0, P1, p))
 
 ##--------------- OR ---------------##
-def distance(P0, P1, p):
+def distance_slower(P0, P1, p):
     T = P1 - P0
     L = (T**2).sum(axis=1)
     U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L
     U = U.reshape(len(U),1)
     D = P0 + U*T - p
     return np.sqrt((D**2).sum(axis=1))
-print(distance(P0, P1, p))
+print(distance_slower(P0, P1, p))
 
 < q79
 Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P[j]) to each line i (P0[i],P1[i])? (★★★)