def WHEREDataTransformation(filename):
global problem_name, percent_name, repeat_name
cluster_file_name = "./Cluster_Data/" + str(problem_name) + "_" + str(percent_name) + "_" + str(repeat_name) + ".txt"
# import pdb
# pdb.set_trace()
if os.path.isfile(cluster_file_name) is True:
print "LOADED FROM THE FILE: ", cluster_file_name
import pickle
cluster_table = pickle.load(open(cluster_file_name, "rb"))
return cluster_table
from utilities.Tools.methods1 import wrapper_createTbl
# The Data has to be access using this attribute table._rows.cells
transformed_table = [[int(z) for z in x.cells[:-1]] + x.cells[-1:] for x in wrapper_createTbl(filename)._rows]
cluster_numbers = set(map(lambda x: x[-1], transformed_table))
#debug
dict = {}
for line in transformed_table:
if line[-1] in dict.keys(): dict[line[-1]] += 1
else: dict[line[-1]] = 1
# separating clusters
# the element looks like [clusterno, rows]
cluster_table = []
for number in cluster_numbers:
cluster_table.append([number]+ [filter(lambda x: x[-1] == number, transformed_table)])
import pickle
pickle.dump(cluster_table, open(cluster_file_name, "wb"))
return cluster_table
def WHEREDataTransformation(filename):
from utilities.Tools.methods1 import wrapper_createTbl
# The Data has to be access using this attribute table._rows.cells
transformed_table = [[int(z) for z in x.cells[:-1]] + x.cells[-1:] for x in wrapper_createTbl(filename)._rows]
cluster_numbers = set(map(lambda x: x[-1], transformed_table))
# separating clusters
# the element looks like [clusterno, rows]
cluster_table = []
for number in cluster_numbers:
cluster_table.append([number] + [filter(lambda x: x[-1] == number, transformed_table)])
return cluster_table
def where_clusterer(filename):
"""
This is function accepts a file with rows(=records) and clusters it. This is FASTNAP + PCA
:param filename: Pass in the filename with rows as valid configurations
:return: List of Cluster. Each cluster has a [[cluster_number], [list of members]]
"""
from utilities.Tools.methods1 import wrapper_createTbl
# The Data has to be access using this attribute table._rows.cells
transformed_table = [[int(z) for z in x.cells[:-1]] + x.cells[-1:] for x in wrapper_createTbl(filename)._rows]
cluster_numbers = set(map(lambda x: x[-1], transformed_table))
# separating clusters
# the element looks like [clusterno, rows]
cluster_table = []
for number in cluster_numbers:
cluster_table.append([number] + [filter(lambda x: x[-1] == number, transformed_table)])
return cluster_table
def where_clusterer(filename):
"""
This is function accepts a file with rows(=records) and clusters it. This is FASTNAP + PCA
:param filename: Pass in the filename with rows as valid configurations
:return: List of Cluster. Each cluster has a [[cluster_number], [list of members]]
"""
from utilities.Tools.methods1 import wrapper_createTbl
# The Data has to be access using this attribute table._rows.cells
transformed_table = [[int(z) for z in x.cells[:-1]] + x.cells[-1:]
for x in wrapper_createTbl(filename)._rows]
cluster_numbers = set(map(lambda x: x[-1], transformed_table))
# separating clusters
# the element looks like [clusterno, rows]
cluster_table = []
for number in cluster_numbers:
cluster_table.append(
[number] + [filter(lambda x: x[-1] == number, transformed_table)])
return cluster_table
def WHEREDataTransformation(filename):
global problem_name, percent_name, repeat_name
cluster_file_name = "./Cluster_Data/" + str(problem_name) + "_" + str(
percent_name) + "_" + str(repeat_name) + ".txt"
# import pdb
# pdb.set_trace()
if os.path.isfile(cluster_file_name) is True:
print "LOADED FROM THE FILE: ", cluster_file_name
import pickle
cluster_table = pickle.load(open(cluster_file_name, "rb"))
return cluster_table
from utilities.Tools.methods1 import wrapper_createTbl
# The Data has to be access using this attribute table._rows.cells
transformed_table = [[int(z) for z in x.cells[:-1]] + x.cells[-1:]
for x in wrapper_createTbl(filename)._rows]
cluster_numbers = set(map(lambda x: x[-1], transformed_table))
#debug
dict = {}
for line in transformed_table:
if line[-1] in dict.keys(): dict[line[-1]] += 1
else: dict[line[-1]] = 1
# separating clusters
# the element looks like [clusterno, rows]
cluster_table = []
for number in cluster_numbers:
cluster_table.append(
[number] + [filter(lambda x: x[-1] == number, transformed_table)])
import pickle
pickle.dump(cluster_table, open(cluster_file_name, "wb"))
return cluster_table