def extractFormulaFeature(query):
"""
Step 1: Query to MathML
"""
math_obj = asciitomathml.asciitomathml.AsciiMathML()
math_obj.parse_string(query)
mathML = math_obj.to_xml_string()
mathML = mathML.replace("<math xmlns=\"http://www.w3.org/1998/Math/MathML\">","<math>")
mathML = mathML.replace("&","")
"""
Step 2: MathML to Formula object
"""
#Extract four types of formula_obj
formula_obj = Formula()
(sem_features, struc_features, const_features, var_features) = features_extraction(mathML)
# Generate index terms
inorder_sem_terms = ino_sem_terms(sem_features)
sorted_sem_terms = sort_sem_terms(sem_features)
#Insert into formulas table
formula_obj.inorder_term = inorder_sem_terms
formula_obj.sorted_term = sorted_sem_terms
formula_obj.structure_term = struc_features
formula_obj.constant_term = const_features
formula_obj.variable_term = var_features
formula_obj.status = 1
"""
Step 3: Extract feature
"""
featureAll = readFeature('all')
formula = formula_obj
formula.structure = []
formula.semantic = []
formula.constant = []
formula.variable = []
#semantic
if formula.sorted_term!= '[]':
f_semantic_array = formula.sorted_term[0]
for line in f_semantic_array:
fa = line.split('$')
for f in fa:
f = semantic_rep(f)
if f != "":
formula.semantic.append(f)
#structure
if formula.structure_term!= '[]':
f_structure_array = formula.structure_term
#print f_structure_array
for f in f_structure_array:
f = struct_rep(f)
if f != "":
formula.structure.append(f)
#constant
if formula.constant_term!= '[]':
f_constant_array = formula.constant_term
#print f_constant_array
for f in f_constant_array:
f = const_rep(f)
if f != "":
formula.constant.append(f)
#variable
if formula.variable_term!= '[]':
f_variable_array = formula.variable_term
for f in f_variable_array:
f = var_rep(f)
if f != "":
formula.variable.append(f)
"""
Step 4: Build vector
"""
line = []
#print formula.semantic
#print formula.structure
#print formula.constant
#print formula.variable
for s in readFeature('semantic'):
line.append(min(1,formula.semantic.count(s)))
for s in readFeature('structure'):
line.append(min(1,formula.structure.count(s)))
for c in readFeature('constant'):
line.append(min(1,formula.constant.count(c)))
for v in readFeature('variable'):
line.append(min(1,formula.variable.count(v)))
return line
def buildVector():
#extract feature term
semantic,structure,constant,variable = get_formula_feature_term()
featureAll = semantic + structure +constant + variable
with open(path+'/data/all.feature','w') as outfile:
json.dump(featureAll,outfile)
all_formula = Formula.objects.all()
all_tag = TagDefinition.objects.all()
for formula in all_formula:
formula.semantic = []
formula.structure = []
formula.constant = []
formula.variable = []
#semantic
if formula.sorted_term!= '[]':
f_semantic_array = formula.sorted_term[1:-1].split(',')
for line in f_semantic_array:
fa = line.split('$')
for f in fa:
f = semantic_rep(f)
if f != "":
formula.semantic.append(f)
#structure
if formula.structure_term!= '[]':
f_structure_array = formula.structure_term[1:-1].split(',')
for f in f_structure_array:
f = struct_rep(f)
if f != "":
formula.structure.append(f)
#constant
if formula.constant_term!= '[]':
f_constant_array = formula.constant_term[1:-1].split(',')
for f in f_constant_array:
f = const_rep(f)
if f != "":
formula.constant.append(f)
#variable
if formula.variable_term!= '[]':
f_variable_array = formula.variable_term[1:-1].split(',')
for f in f_variable_array:
f = var_rep(f)
if f != "":
formula.variable.append(f)
#------------------ Build data vector ------------------------------
term_matrix = []
for f in all_formula:
line = []
line.append(int(f.indexid))
print line
for s in semantic:
line.append(min(1,f.semantic.count(s)))
for s in structure:
line.append(min(1,f.structure.count(s)))
for c in constant:
line.append(min(1,f.constant.count(c)))
for v in variable:
line.append(min(1,f.variable.count(v)))
#tag
"""
f.tagdef = Tag.objects.filter(question_id = f.id)
f.tagid = []
for tag in f.tagdef:
f.tagid.append(int(tag.tagdefinition.id))
for t in all_tag:
line.append(min(1,f.tagid.count(t.id)))
"""
term_matrix.append(line)
with open(path+'/data/data.vector','w') as outfile:
json.dump(term_matrix,outfile)
with open(path+'/data/data2.vector','w') as outfile:
for line in term_matrix:
for item in line:
outfile.write(str(item))
outfile.write("\t")
outfile.write("\n")
return