[TensorFlow & Python] 파이썬 문장을 분해해서 보고 싶습니다.

# find mean value.
## means = tf.concat(0, [tf.reduce_mean(tf.gather(vectors, tf.reshape(tf.where(tf.equal(assignments, c)), [1, -1])), reduction_indices=[1]) for c in xrange(k)])

print "Finding mean value..."
# find mean value.
## means = tf.concat(0, [tf.reduce_mean(tf.gather(vectors, tf.reshape(tf.where(tf.equal(assignments, c)), [1, -1])), reduction_indices=[1]) for c in xrange(k)])
for c in xrange(k):
	tf_equal = tf.equal(assignments, c)											# 
	tf_where = tf.where(tf_equal)												# 
	tf_reshape = tf.reshape(tf_where, [1, -1])										# 
	tf_gather = tf.gather(vectors, tf_reshape)										# 
	tf_reduce_mean = tf.reduce_mean(tf_gather, reduction_indices=[1])				# 
	tf_concat_range = tf_reduce_mean											# 
	means = tf.concat(0, tf_concat_range)										#

# import numerical python library.
import numpy as np
 
# preparations.
num_points = 8 # 2000
vectors_set = []
for i in xrange(num_points): # vectors_set appends 2000 2-dimensional vectors.
	if np.random.random() > 0.5:
		vectors_set.append([np.random.normal(0.0, 0.9), np.random.normal(0.0, 0.9)])
	else:
		vectors_set.append([np.random.normal(3.0, 0.5), np.random.normal(1.0, 0.5)])
 
# use seaborn visualizer package and pandas data controller package.
import matplotlib.pyplot as plot
import pandas as pd
import seaborn as sns
 
# df = pd.DataFrame({"x": [v[0] for v in vectors_set], "y": [v[1] for v in vectors_set]})
# sns.lmplot("x", "y", data=df, fit_reg=False, size=6)
# plot.show()
 
# use TensorFlow.
import tensorflow as tf
 
vectors = tf.constant(vectors_set) 													# create constant tensor with randomly created data.
k = 4																				# define K as 4.
centroides = tf.Variable(tf.slice(tf.random_shuffle(vectors), [0, 0], [k, -1]))		# TensorFlow selects K centroides.
 
vectors_set = [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [1.1, 2.2], [3.3, 4.4], [5.5, 6.6], [7.7, 8.8]]
vectors = tf.constant(vectors_set)
centroides = tf.Variable([[3.0, 4.0], [5.0, 6.0], [1.0, 2.0], [5.5, 6.6]])
 
## !!!!! NOTE !!!!!
## vectors is from tf.constant(vectors_set) which makes constant tensor (it means that it does not change input vector's dimension)
## tf.random_shuffle does shuffle input vectors based on first dimension.
## tf.slice removes tensor's designated parts. It makes centroides as [k, 2] dimensional matrix.
 
 
# expand tensors' dimensions to make tensors 3-dimensional data, which makes it possible to operate subtraction between 2 tensors.
expanded_vectors = tf.expand_dims(vectors, 0)										# change 2-dimension matrix vectors(2000x2) to 3-dimension matrix(D0x2000x2).
expanded_centroides = tf.expand_dims(centroides, 1)									# change 2-dimension matrix centroides(4x2) to 3-dimension matrix(4xD1x2).
## !!!!! NOTE !!!!!
## tf.sub function can know how to subtract both tensors elements itself using its broadcasting feature.
## 1-dimension tensor is operated iteratively based on other tensors correspond to matching dimension.
## In this case, expanded_vectors has 3-dimension matrix which size is (D0x2000x2) and expanded_centroides's one is (4xD1x2).
## tf.sub calculates with these tensors supposing that D0 is 4 and D1 is 2000. Third dimension's size is both equal and can be calculated as-is.
 
 
# find Euclidean distances.
## assignments = tf.argmin(tf.reduce_sum(tf.square(tf.sub(expanded_vectors, expanded_centroides)), 2), 0)
diff = tf.sub(expanded_vectors, expanded_centroides)								# subtract 2 tensors element-wise.
square_diff = tf.square(diff)														# get squared difference value. It is also element-wise.
distances = tf.reduce_sum(square_diff, 2)											# reduce dimension 2(D2); ((xc-xi)^2, (yc-yi)^2) -> ((xc-xi)^2+(yc-yi)^2) for all (4x2000) elements.
assignments = tf.argmin(distances, 0)												# tf.argmin returns the index with the smallest value across dimensions of a tensor.
 
 
print "Finding mean value..."
# find mean value.
## means = tf.concat(0, [tf.reduce_mean(tf.gather(vectors, tf.reshape(tf.where(tf.equal(assignments, c)), [1, -1])), reduction_indices=[1]) for c in xrange(k)])
for c in xrange(k):
	tf_equal = tf.equal(assignments, c)												# 
	tf_where = tf.where(tf_equal)													# 
	tf_reshape = tf.reshape(tf_where, [1, -1])										# 
	tf_gather = tf.gather(vectors, tf_reshape)										# 
	tf_reduce_mean = tf.reduce_mean(tf_gather, reduction_indices=[1])				# 
	tf_concat_range = tf_reduce_mean												# 
	means = tf.concat(0, tf_concat_range)											#
 
# 
update_centroides = tf.assign(centroides, means)
 
 
# initializations.
init_op = tf.initialize_all_variables()
session = tf.Session()
session.run(init_op)
for step in xrange(3):
	## use these lines to debug each tensor variables' outputs.
	## print step, "th loop time; "
	## re-write this line into one line.
	## _, centroid_values, assignment_values,
	##     vs, cs, evs, ecs, out_diff, out_sdiff, out_dist =
	##     session.run([update_centroides, centroides, assignments,
	##                  vectors, centroides, expanded_vectors, expanded_centroides, diff, square_diff, distances])
	## =================================================
	'''
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print "vectors: "
	print vs
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print "centors: "
	print cs
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print "expanded_vectors: "
	print evs
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print "expanded_centors: "
	print ecs
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print "diff: " 
	print out_diff
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print "square_diff: " 
	print out_sdiff
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print "distances: " 
	print out_dist
	'''
	## =================================================
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print centroid_values
	print ">>>>>>>>>>>>>>>>>>>>>>>>"
	print assignment_values
	print ""
 
# executions.
data = {"x": [], "y": [], "cluster": []}
for i in xrange(len(assignment_values)):
	data["x"].append(vectors_set[i][0])
	data["y"].append(vectors_set[i][1])
	data["cluster"].append(assignment_values[i])
 
# end of program.
df = pd.DataFrame(data)
sns.lmplot("x", "y", data=df, fit_reg=False, size=6, hue="cluster", legend=False)
plot.show()