def get_kmedoids(df, dist_matrix, num_clusters):
clusterid, error, nfound = Cluster.kmedoids(dist_matrix, num_clusters)
df = df.reset_index()
df['cluster_id'] = pd.DataFrame(data=clusterid)
return df
def get_kmedoids(input_points, num_clusters):
"""
df = pd.read_csv(fname)
x = df.iloc[:, 0]
y = df.iloc[:, 1]
inputs = []
for row in range(len(df)):
inputs.append((df.iloc[row, 0], df.iloc[row, 1]))
"""
inputs = input_points
google_key = "AIzaSyBIrorNpUJ_RyaxZ-8PxC0ZXZ818hRc5hM"
maps = googlemaps.Client(key=google_key)
# inputs = inputs[:5] # for testing
dist_matrix = tmo.get_dist_matrix(inputs, maps)
clusterid, error, nfound = Cluster.kmedoids(array(dist_matrix), nclusters=num_clusters)
# calculate cluster centroid
clusterid_set = set(clusterid)
centroids_dict = {}
centroids_arr = []
for itm in clusterid_set:
k = itm
val = inputs[itm]
centroids_dict[k] = val
centroids_arr.append(val)
# a,b = Cluster.clustercentroids(clusterid=clusterid)
centroids_arr = np.array(map(np.array, centroids_arr))
# convert dist_matrix to a dataframe
yard_index = ['turf'+str(i) for i in range(1, len(inputs)+1)]
turf_index = ['turf'+str(i) for i in range(1, len(inputs)+1)]
dist_mat = array(dist_matrix)
df_dist_matr = pd.DataFrame(data=dist_mat[0:,0:],
index=yard_index,
columns=turf_index)
return centroids_arr, df_dist_matr
kmeans1 = KMeans(n_clusters=5, random_state=0).fit(dist_dot_product)
kmeans2 = KMeans(n_clusters=5, random_state=0).fit(general_dot_product)
image_kmeans_dot = np.zeros((ys[1] - ys[0], xs[1] - xs[0]))
image_kmeans_pixel = np.zeros((ys[1] - ys[0], xs[1] - xs[0]))
print('Transforming in real coordinates...')
for i in ids:
image_kmeans_dot[parser.coordinates[i][1] -
ys[0]][parser.coordinates[i][0] -
xs[1]] = kmeans1.labels_[ids.index(i)]
image_kmeans_pixel[parser.coordinates[i][1] -
ys[0]][parser.coordinates[i][0] -
xs[1]] = kmeans2.labels_[ids.index(i)]
print('Kmedoid clustering...')
kmedoid_dot = Cluster.kmedoids(dist_dot_product,
nclusters=5,
npass=5,
initialid=None) #Distance Matrix!!!
kmedoid_pixel = Cluster.kmedoids(general_dot_product,
nclusters=5,
npass=5,
initialid=None) #Distance Matrix!!!
image_kmedoid_dot = np.zeros((ys[1] - ys[0], xs[1] - xs[0]))
image_kmedoid_pixel = np.zeros((ys[1] - ys[0], xs[1] - xs[0]))
for i in ids:
image_kmedoid_dot[parser.coordinates[i][1] -
ys[0]][parser.coordinates[i][0] -
xs[1]] = kmedoid_dot[0][ids.index(i)]
image_kmedoid_pixel[parser.coordinates[i][1] -
ys[0]][parser.coordinates[i][0] -
xs[1]] = kmedoid_pixel[0][ids.index(i)]
