def __call__(self):
"""
Do an iteration of the Lloyd algorithm.
"""
centers = np.array(list(self.gs.init_strategy(
self.gs.points, self.gs.nclusters)))
sqdists = kmeans.get_point_center_sqdists(self.gs.points, centers)
labels = kmeans.get_labels(sqdists)
wcss, labels = kmeans.lloyd(self.gs.points, labels)
if (self.best_wcss is None) or (wcss < self.best_wcss):
self.best_wcss = wcss
self.best_labels = labels
# do not stop
return False
def get_response_content(fs):
# define constants
nrestarts = 10
# read the input
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
points = get_rtable_info(rtable, "annotation", fs.axes)
# do the clustering
codebook, distortion = cluster.vq.kmeans(points, fs.k, iter=nrestarts, thresh=1e-9)
sqdists = kmeans.get_point_center_sqdists(points, codebook)
labels = kmeans.get_labels_without_cluster_removal(sqdists)
wgss = kmeans.get_wcss(sqdists, labels)
norms = [np.linalg.norm(p - codebook[g]) for p, g in zip(points, labels)]
redistortion = np.mean(norms)
# create the response
out = StringIO()
print >> out, "scipy distortion:", distortion
print >> out, "recomputed distortion:", redistortion
print >> out, "wgss:", wgss
# return the response
return out.getvalue()
def get_response_content(fs):
# define constants
nrestarts = 10
# read the input
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
points = get_rtable_info(rtable, 'annotation', fs.axes)
# do the clustering
codebook, distortion = cluster.vq.kmeans(
points, fs.k, iter=nrestarts, thresh=1e-9)
sqdists = kmeans.get_point_center_sqdists(points, codebook)
labels = kmeans.get_labels_without_cluster_removal(sqdists)
wgss = kmeans.get_wcss(sqdists, labels)
norms = [np.linalg.norm(p-codebook[g]) for p, g in zip(points, labels)]
redistortion = np.mean(norms)
# create the response
out = StringIO()
print >> out, 'scipy distortion:', distortion
print >> out, 'recomputed distortion:', redistortion
print >> out, 'wgss:', wgss
# return the response
return out.getvalue()
def get_response_content(fs):
# read the table
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
Carbone.validate_headers(header_row)
# get the numpy array of conformant points
h_to_i = dict((h, i + 1) for i, h in enumerate(header_row))
axis_headers = fs.axes
if not axis_headers:
raise ValueError('no Euclidean axes were provided')
axis_set = set(axis_headers)
header_set = set(header_row)
bad_axes = axis_set - header_set
if bad_axes:
raise ValueError('invalid axes: ' + ', '.join(bad_axes))
axis_lists = []
for h in axis_headers:
index = h_to_i[h]
try:
axis_list = Carbone.get_numeric_column(data_rows, index)
except Carbone.NumericError:
msg_a = 'expected the axis column %s ' % h
msg_b = 'to be numeric'
raise ValueError(msg_a + msg_b)
axis_lists.append(axis_list)
points = np.array(zip(*axis_lists))
# precompute some stuff
allmeandist = kmeans.get_allmeandist(points)
nrestarts = 10
nseconds = 2
tm = time.time()
n = len(points)
wgss_list = []
# neg because both items in the pair are used for sorting
neg_calinski_k_pairs = []
# look for the best calinski index in a small amount of time
k = 2
while True:
codebook, distortion = cluster.vq.kmeans(points,
k,
iter=nrestarts,
thresh=1e-9)
sqdists = kmeans.get_point_center_sqdists(points, codebook)
labels = kmeans.get_labels_without_cluster_removal(sqdists)
wgss = kmeans.get_wcss(sqdists, labels)
bgss = allmeandist - wgss
calinski = kmeans.get_calinski_index(bgss, wgss, k, n)
k_unique = len(set(labels))
neg_calinski_k_pairs.append((-calinski, k_unique))
wgss_list.append(wgss)
if time.time() - tm > nseconds:
break
if k == n - 1:
break
k += 1
max_k = k
best_neg_calinski, best_k = min(neg_calinski_k_pairs)
best_calinski = -best_neg_calinski
# create the response
out = StringIO()
print >> out, 'best cluster count: k = %d' % best_k
print >> out, 'searched 2 <= k <= %d clusters' % max_k
print >> out, '%.2f seconds' % (time.time() - tm)
if fs.verbose:
print >> out
print >> out, '(k_unique, wgss, calinski):'
for wgss, neg_calinski_k_pair in zip(wgss_list, neg_calinski_k_pairs):
neg_calinski, k_unique = neg_calinski_k_pair
calinski = -neg_calinski
row = [k_unique, wgss, calinski]
print >> out, '\t'.join(str(x) for x in row)
# return the response
return out.getvalue()
def get_response_content(fs):
# read the table
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
Carbone.validate_headers(header_row)
# get the numpy array of conformant points
h_to_i = dict((h, i+1) for i, h in enumerate(header_row))
axis_headers = fs.axes
if not axis_headers:
raise ValueError('no Euclidean axes were provided')
axis_set = set(axis_headers)
header_set = set(header_row)
bad_axes = axis_set - header_set
if bad_axes:
raise ValueError('invalid axes: ' + ', '.join(bad_axes))
axis_lists = []
for h in axis_headers:
index = h_to_i[h]
try:
axis_list = Carbone.get_numeric_column(data_rows, index)
except Carbone.NumericError:
msg_a = 'expected the axis column %s ' % h
msg_b = 'to be numeric'
raise ValueError(msg_a + msg_b)
axis_lists.append(axis_list)
points = np.array(zip(*axis_lists))
# precompute some stuff
allmeandist = kmeans.get_allmeandist(points)
nrestarts = 10
nseconds = 2
tm = time.time()
n = len(points)
wgss_list = []
# neg because both items in the pair are used for sorting
neg_calinski_k_pairs = []
# look for the best calinski index in a small amount of time
k = 2
while True:
codebook, distortion = cluster.vq.kmeans(
points, k, iter=nrestarts, thresh=1e-9)
sqdists = kmeans.get_point_center_sqdists(points, codebook)
labels = kmeans.get_labels_without_cluster_removal(sqdists)
wgss = kmeans.get_wcss(sqdists, labels)
bgss = allmeandist - wgss
calinski = kmeans.get_calinski_index(bgss, wgss, k, n)
k_unique = len(set(labels))
neg_calinski_k_pairs.append((-calinski, k_unique))
wgss_list.append(wgss)
if time.time() - tm > nseconds:
break
if k == n-1:
break
k += 1
max_k = k
best_neg_calinski, best_k = min(neg_calinski_k_pairs)
best_calinski = -best_neg_calinski
# create the response
out = StringIO()
print >> out, 'best cluster count: k = %d' % best_k
print >> out, 'searched 2 <= k <= %d clusters' % max_k
print >> out, '%.2f seconds' % (time.time() - tm)
if fs.verbose:
print >> out
print >> out, '(k_unique, wgss, calinski):'
for wgss, neg_calinski_k_pair in zip(wgss_list, neg_calinski_k_pairs):
neg_calinski, k_unique = neg_calinski_k_pair
calinski = -neg_calinski
row = [k_unique, wgss, calinski]
print >> out, '\t'.join(str(x) for x in row)
# return the response
return out.getvalue()