def modified_constant_increment(data, beta, space_size=(-20, 20), step=1):
lines = get_lines_modified_constant_increment(data, beta)
repre = representatives(data, lines)
trypoints = generate_points(space_size[0], space_size[1], step)
classified = classify_ross(lines, trypoints, repre)
data = merge_dicts(data, classified)
return data
def rossenblatt(data, space_size=(-20, 20), step=1):
lines = get_lines_ross(data)
# plot_lines(lines) # needs some fixing, but algorithm is ok
repre = representatives(data, lines)
trypoints = generate_points(space_size[0], space_size[1], step)
classified = classify_ross(lines, trypoints, repre)
data = merge_dicts(data, classified)
return data
def minimal_distance(data, classes, space_size=(-20, 20), step=1):
dist = kmeans(data, classes)
trypoints = generate_points(space_size[0], space_size[1], step)
for point in trypoints:
distances = {key: distanc(point, key)
for key in dist.keys()
} # dict for each point -> key: distance to him
key_of_min = min(distances.keys(), key=(
lambda key: distances[key])) # select key with minimum distance
dist[key_of_min].append(point) # add point to this key
return dist
def nearest_neighbour(data, classes, space_size=(-20, 20), step=1):
k_means = kmeans(data, classes)
trypoints = generate_points(space_size[0], space_size[1], step)
points_in_kmeans = list(itertools.chain(*k_means.values()))
kmeans_toplot = dict(k_means)
for trypoint in trypoints:
sorted_means = sorted(points_in_kmeans,
key=lambda p: distanc(trypoint, p))
for key, value in k_means.items():
for val in value:
if val == sorted_means[0]:
kmeans_toplot[key].append(trypoint)
return kmeans_toplot
def knearest_neighbour(data, classes, space_size=(-20, 20), step=1):
k_means = kmeans(data, classes)
trypoints = generate_points(space_size[0], space_size[1], step)
means_toplot = dict(k_means)
for trypoint in trypoints:
for val in k_means.values():
val.sort(key=lambda p: distanc(trypoint, p))
newdict = {
key: average_dist(trypoint, k_means[key])
for key in k_means.keys()
}
keywithminvalue = min(newdict, key=newdict.get)
means_toplot[keywithminvalue].append(trypoint)
return means_toplot
def bayes(data, classes, space_size=(-20, 20), step=1):
"""
:param data:
:param classes:
:param space_size:
:param float step:
:return:
"""
dist = kmeans(data, classes)
trypoints = generate_points(space_size[0], space_size[1], step)
prob = probability(dist)
prumery = means(dist)
sigm = sigma(dist, prumery)
for point in trypoints:
rozhodovaci = {key: normal(point, sigm[key], prumery[key], prob[key]) for key in dist.keys()}
keywithmaxvalue = max(rozhodovaci, key=rozhodovaci.get)
dist[keywithmaxvalue].append(point)
return dist