def metric_entropy(x, y, name=None, type_map=None, direction=None):
if (sum(x) == 0):
return 1
#x = np.array(x[:])
#y = np.array(y[:])
#x = np.ndarray.flatten(x)
#y = np.ndarray.flatten(y)
if (direction == 0):
x_type = type_map.loc[name]["A type"]
y_type = type_map.loc[name]["B type"]
else:
x_type = type_map.loc[name]["B type"]
y_type = type_map.loc[name]["A type"]
if (x_type == "Numerical"):
x = cluster(x)
if (y_type == "Numerical"):
y = cluster(y)
data = np.zeros((2, len(x)))
#print name, x_type,y_type,len(x),type(x), "data.shape"
data[0] = x
data[1] = y
it = InformationTheoryTool(data)
entropy = float(it.single_entropy(0, 2)) / log(float(count_unique(x)), 2)
H = float(entropy)
return H
def metric_entropy(x, y, name=None, type_map=None, direction=None):
if sum(x) == 0:
return 1
# x = np.array(x[:])
# y = np.array(y[:])
# x = np.ndarray.flatten(x)
# y = np.ndarray.flatten(y)
if direction == 0:
x_type = type_map.loc[name]["A type"]
y_type = type_map.loc[name]["B type"]
else:
x_type = type_map.loc[name]["B type"]
y_type = type_map.loc[name]["A type"]
if x_type == "Numerical":
x = cluster(x)
if y_type == "Numerical":
y = cluster(y)
data = np.zeros((2, len(x)))
# print name, x_type,y_type,len(x),type(x), "data.shape"
data[0] = x
data[1] = y
it = InformationTheoryTool(data)
entropy = float(it.single_entropy(0, 2)) / log(float(count_unique(x)), 2)
H = float(entropy)
return H
def mutual_information(x, y, name=None, type_map=None, direction=None):
if (sum(y) == 0):
return 0
#x = np.array(x[:])
#y = np.array(y[:])
#x = np.ndarray.flatten(x)
#y = np.ndarray.flatten(y)
if (direction == 0):
x_type = type_map.loc[name]["A type"]
y_type = type_map.loc[name]["B type"]
else:
x_type = type_map.loc[name]["B type"]
y_type = type_map.loc[name]["A type"]
if (x_type == "Numerical"):
x = cluster(x)
if (y_type == "Numerical"):
y = cluster(y)
data = np.zeros((2, len(x)))
#print name, x_type,y_type,len(x),type(x), "data.shape"
data[0] = x
data[1] = y
it = InformationTheoryTool(data)
mutual_info = it.mutual_information(0, 1, 2)
#print len(y)
#g = float(mi)/(2.0*len(y))
#print x,y
# entropy = it.single_entropy(0,2)
# if(entropy == 0):
# return 1000
C = float(mutual_info)
return C
def mutual_information(x, y, name=None, type_map=None, direction=None):
if sum(y) == 0:
return 0
# x = np.array(x[:])
# y = np.array(y[:])
# x = np.ndarray.flatten(x)
# y = np.ndarray.flatten(y)
if direction == 0:
x_type = type_map.loc[name]["A type"]
y_type = type_map.loc[name]["B type"]
else:
x_type = type_map.loc[name]["B type"]
y_type = type_map.loc[name]["A type"]
if x_type == "Numerical":
x = cluster(x)
if y_type == "Numerical":
y = cluster(y)
data = np.zeros((2, len(x)))
# print name, x_type,y_type,len(x),type(x), "data.shape"
data[0] = x
data[1] = y
it = InformationTheoryTool(data)
mutual_info = it.mutual_information(0, 1, 2)
# print len(y)
# g = float(mi)/(2.0*len(y))
# print x,y
# entropy = it.single_entropy(0,2)
# if(entropy == 0):
# return 1000
C = float(mutual_info)
return C
