def part1():
points = get_input()
sparse = get_sparse_matrix(points).tocsc()
dense = np.asarray(sparse.toarray(), dtype=str)
for coord, _ in np.ndenumerate(dense):
v = closest_node_value(coord, sparse)
dense[coord] = v
borders = [dense[0], dense[-1], dense[:, 0], dense[:, -1]]
infinite = set()
for line in borders:
for c in line:
infinite.add(c)
char, counts = np.unique(dense, return_counts=True)
possible = {c: cnt for c, cnt in zip(char, counts) if c not in infinite}
return max(possible.items(), key=lambda x: x[1])
def entropy2(x, y):
"""Joint entropy of paired samples X and Y"""
#
# Bin each image into 256 gray levels
#
x = (stretch(x) * 255).astype(int)
y = (stretch(y) * 255).astype(int)
#
# create an image where each pixel with the same X & Y gets
# the same value
#
xy = 256 * x + y
xy = xy.flatten()
sparse = scipy.sparse.coo_matrix((np.ones(xy.shape), (xy, np.zeros(xy.shape))))
histogram = sparse.toarray()
n = np.sum(histogram)
if n > 0 and np.max(histogram) > 0:
histogram = histogram[histogram > 0]
return np.log2(n) - old_div(np.sum(histogram * np.log2(histogram)), n)
else:
return 0
def entropy2(x,y):
'''Joint entropy of paired samples X and Y'''
#
# Bin each image into 256 gray levels
#
x = (stretch(x) * 255).astype(int)
y = (stretch(y) * 255).astype(int)
#
# create an image where each pixel with the same X & Y gets
# the same value
#
xy = 256*x+y
xy = xy.flatten()
sparse = scipy.sparse.coo_matrix((np.ones(xy.shape),
(xy,np.zeros(xy.shape))))
histogram = sparse.toarray()
n=np.sum(histogram)
if n > 0 and np.max(histogram) > 0:
histogram = histogram[histogram>0]
return np.log2(n) - np.sum(histogram * np.log2(histogram)) / n
else:
return 0
