if file_name == 'z':
break
raw_features = extract.start(file_name)
raw_baseline_angle = raw_features[0]
baseline_angle, comment = categorize.determine_baseline_angle(
raw_baseline_angle)
print "Baseline Angle: " + comment
raw_top_margin = raw_features[1]
top_margin, comment = categorize.determine_top_margin(raw_top_margin)
print "Top Margin: " + comment
raw_letter_size = raw_features[2]
letter_size, comment = categorize.determine_letter_size(
raw_letter_size)
print "Letter Size: " + comment
raw_line_spacing = raw_features[3]
line_spacing, comment = categorize.determine_line_spacing(
raw_line_spacing)
print "Line Spacing: " + comment
raw_word_spacing = raw_features[4]
word_spacing, comment = categorize.determine_word_spacing(
raw_word_spacing)
print "Word Spacing: " + comment
raw_pen_pressure = raw_features[5]
pen_pressure, comment = categorize.determine_pen_pressure(
raw_pen_pressure)
def learning():
score1=0
score2=0
score3=0
score4=0
score5=0
score6=0
score7=0
score8=0
if request.method=='POST':
data1=request.form['userinput1']
data2=request.form['userinput2']
data3=request.form['userinput3']
data4=request.form['userinput4']
data5=request.form['userinput5']
response1=int(sc_tf_idf.classify(process_message(data1)))
response2=int(sc_tf_idf.classify(process_message(data2)))
response3=int(sc_tf_idf.classify(process_message(data3)))
response4=int(sc_tf_idf.classify(process_message(data4)))
response5=int(sc_tf_idf.classify(process_message(data5)))
score=0.2*(response1+response2+response3+response4+response5)
sex=request.form['sex']
age=request.form['age']
p_status=request.form['p_status']
mjob=request.form['mjob']
fjob=request.form['fjob']
reason=request.form['reason']
stime=request.form['stime']
fail=request.form['fail']
famsup=request.form['famsup']
act=request.form['act']
net=request.form['net']
rel=request.form['rel']
famrel=request.form['famrel']
free=request.form['free']
goout=request.form['goout']
dalc=request.form['dalc']
walc=request.form['walc']
health=request.form['health']
abscences=request.form['abscences']
cgpa=request.form['cgpa']
data=[sex,age,p_status,mjob,fjob,reason,stime,fail,famsup,act,net,rel,famrel,free,goout,dalc,walc,health,abscences]
with open('testing.csv','a') as fd:
for i in data:
fd.write(i)
fd.write(',')
fd.write(cgpa)
fd.write('\n')
fd.close()
student_data = pd.read_csv("students.csv", encoding='utf-8')
test = pd.read_csv("testing.csv", encoding='utf-8')
n_students = student_data.shape[0]
n_features = student_data.shape[1] - 1
n_passed = student_data["risk"].value_counts()[1]
n_failed = student_data["risk"].value_counts()[0]
grad_rate = float(n_passed)/n_students*100
feature_cols = list(student_data.columns[:-1]) # all columns but last are features
target_col = student_data.columns[-1] # last column is the target/label
X_all = student_data[feature_cols] # feature values for all students
y_all = student_data[target_col] # corresponding targets/labels
test_example = test[feature_cols]
X_all = dews.preprocess_features(X_all)
test_example = dews.preprocess_features(test_example)
test_example = test_example.iloc[-1:]
num_all = student_data.shape[0] # same as len(student_data)
num_train = 316 # about 80% of the data
num_test = 79
X_train, X_test, y_train, y_test = train_test_split(X_all,y_all,train_size=num_train,test_size=num_test,stratify=y_all)
f1_scorer = make_scorer(f1_score, pos_label=1)
parameters = {'max_depth': range(1,15)}
dt = DecisionTreeClassifier()
grid_search = GridSearchCV(dt,parameters,scoring=f1_scorer)
grid_search.fit(X_train,y_train)
dt_tuned = DecisionTreeClassifier(max_depth=3)
dt_tuned.fit(X_train,y_train)
# Subset the Dataset by removing features whose 'importance' is zero,
# according to a tuned Decision tree in 1.1
sub = np.nonzero(dt_tuned.feature_importances_)[0].tolist()
subset_cols = list(X_train.columns[sub])
X_train_subset = X_train[subset_cols]
X_test_subset = X_test[subset_cols]
test_example_subset = test_example[subset_cols]
clf_default = KNeighborsClassifier()
# Determine the number of nearest neighbors that optimizes accuracy
parameters = {'n_neighbors': range(1,30)}
knn = KNeighborsClassifier()
knn_tuned = GridSearchCV(knn,parameters,scoring=f1_scorer)
knn_tuned.fit(X_train_subset,y_train)
clf_tuned = KNeighborsClassifier(n_neighbors=knn_tuned.best_params_['n_neighbors'])
y = dews.train_predict("Subset_KNN", X_train_subset, y_train, X_test_subset, y_test, test_example_subset, 300, clf_default, clf_tuned)
file1 = request.files['file1']
file2 = request.files['file2']
file3 = request.files['file3']
file4 = request.files['file4']
file5 = request.files['file5']
if file1 and allowed_file(file1.filename) and file2 and allowed_file(file2.filename) and file3 and allowed_file(file3.filename) and file4 and allowed_file(file4.filename) and file5 and allowed_file(file5.filename):
file_names=[secure_filename(file1.filename),secure_filename(file2.filename),secure_filename(file3.filename),secure_filename(file4.filename),secure_filename(file5.filename)]
for file_name in file_names:
raw_features = extract.start(file_name)
raw_baseline_angle = raw_features[0]
baseline_angle, comment = categorize.determine_baseline_angle(raw_baseline_angle)
#print ("Baseline Angle: "+comment)
raw_top_margin = raw_features[1]
top_margin, comment = categorize.determine_top_margin(raw_top_margin)
#print ("Top Margin: "+comment)
raw_letter_size = raw_features[2]
letter_size, comment = categorize.determine_letter_size(raw_letter_size)
#print ("Letter Size: "+comment)
raw_line_spacing = raw_features[3]
line_spacing, comment = categorize.determine_line_spacing(raw_line_spacing)
#print ("Line Spacing: "+comment)
raw_word_spacing = raw_features[4]
word_spacing, comment = categorize.determine_word_spacing(raw_word_spacing)
#print ("Word Spacing: "+comment)
raw_pen_pressure = raw_features[5]
pen_pressure, comment = categorize.determine_pen_pressure(raw_pen_pressure)
#print ("Pen Pressure: "+comment)
raw_slant_angle = raw_features[6]
slant_angle, comment = categorize.determine_slant_angle(raw_slant_angle)
#print ("Slant: "+comment)
#print
score1+=0.2*clf1.predict([[baseline_angle, slant_angle]])[0]
score2+=0.2*clf2.predict([[letter_size, pen_pressure]])[0]
score3+=0.2*clf3.predict([[letter_size, top_margin]])[0]
score4+=0.2*clf4.predict([[line_spacing, word_spacing]])[0]
score5+=0.2*clf5.predict([[slant_angle, top_margin]])[0]
score6+=0.2*clf6.predict([[letter_size, line_spacing]])[0]
score7+=0.2*clf7.predict([[letter_size, word_spacing]])[0]
score8+=0.2*clf8.predict([[line_spacing, word_spacing]])[0]
score_hand=[str(round(score1,2)),str(round(score2,2)),str(round(score3,2)),str(round(score4,2)),str(round(score5,2)),str(round(score6,2)),str(round(score7,2)),str(round(score8,2))]
max1=0
max2=0
i1=0
i2=0
print(score_hand)
for i in range(8):
if float(score_hand[i]) > max1:
max1 = float(score_hand[i])
i1=i
if float(score_hand[i]) > max2 and float(score_hand[i]) <= max1 and i!=i1:
max2 = float(score_hand[i])
i2=i
array=['emotional stability','mental energy or will power','modesty','personal harmony and flexibility','lack of discipline','poor concentration','non communicativeness','social isolation']
final_score=[fuzzy(scoring(score_hand),score,y),array[i1],max1,array[i2],max2]
return render_template('result.html', result=final_score)
def predict_all(file_name):
result = {}
X_baseline_angle = []
X_top_margin = []
X_letter_size = []
X_line_spacing = []
X_word_spacing = []
X_pen_pressure = []
X_slant_angle = []
y_t1 = []
y_t2 = []
y_t3 = []
y_t4 = []
y_t5 = []
y_t6 = []
y_t7 = []
y_t8 = []
page_ids = []
if os.path.isfile("label_list"):
print(("Info: label_list found."))
# =================================================================
with open('label_list', "r") as labels:
for line in labels:
content = line.split()
baseline_angle = float(content[0])
X_baseline_angle.append(baseline_angle)
top_margin = float(content[1])
X_top_margin.append(top_margin)
letter_size = float(content[2])
X_letter_size.append(letter_size)
line_spacing = float(content[3])
X_line_spacing.append(line_spacing)
word_spacing = float(content[4])
X_word_spacing.append(word_spacing)
pen_pressure = float(content[5])
X_pen_pressure.append(pen_pressure)
slant_angle = float(content[6])
X_slant_angle.append(slant_angle)
trait_1 = float(content[7])
y_t1.append(trait_1)
trait_2 = float(content[8])
y_t2.append(trait_2)
trait_3 = float(content[9])
y_t3.append(trait_3)
trait_4 = float(content[10])
y_t4.append(trait_4)
trait_5 = float(content[11])
y_t5.append(trait_5)
trait_6 = float(content[12])
y_t6.append(trait_6)
trait_7 = float(content[13])
y_t7.append(trait_7)
trait_8 = float(content[14])
y_t8.append(trait_8)
page_id = content[15]
page_ids.append(page_id)
#===============================================================
# emotional stability
X_t1 = []
for a, b in itertools.zip_longest(X_baseline_angle, X_slant_angle):
X_t1.append([a, b])
# mental energy or will power
X_t2 = []
for a, b in itertools.zip_longest(X_letter_size, X_pen_pressure):
X_t2.append([a, b])
# modesty
X_t3 = []
for a, b in itertools.zip_longest(X_letter_size, X_top_margin):
X_t3.append([a, b])
# personal harmony and flexibility
X_t4 = []
for a, b in itertools.zip_longest(X_line_spacing, X_word_spacing):
X_t4.append([a, b])
# lack of discipline
X_t5 = []
for a, b in itertools.zip_longest(X_slant_angle, X_top_margin):
X_t5.append([a, b])
# poor concentration
X_t6 = []
for a, b in itertools.zip_longest(X_letter_size, X_line_spacing):
X_t6.append([a, b])
# non communicativeness
X_t7 = []
for a, b in itertools.zip_longest(X_letter_size, X_word_spacing):
X_t7.append([a, b])
# social isolation
X_t8 = []
for a, b in itertools.zip_longest(X_line_spacing, X_word_spacing):
X_t8.append([a, b])
#print(X_t1)
#print(type(X_t1))
#print(len(X_t1))
X_train, X_test, y_train, y_test = train_test_split(X_t1,
y_t1,
test_size=.30,
random_state=8)
clf1 = SVC(kernel='rbf')
clf1.fit(X_train, y_train)
print("Classifier 1 accuracy: ",
accuracy_score(clf1.predict(X_test), y_test))
X_train, X_test, y_train, y_test = train_test_split(X_t2,
y_t2,
test_size=.30,
random_state=16)
clf2 = SVC(kernel='rbf')
clf2.fit(X_train, y_train)
print("Classifier 2 accuracy: ",
accuracy_score(clf2.predict(X_test), y_test))
X_train, X_test, y_train, y_test = train_test_split(X_t3,
y_t3,
test_size=.30,
random_state=32)
clf3 = SVC(kernel='rbf')
clf3.fit(X_train, y_train)
print("Classifier 3 accuracy: ",
accuracy_score(clf3.predict(X_test), y_test))
X_train, X_test, y_train, y_test = train_test_split(X_t4,
y_t4,
test_size=.30,
random_state=64)
clf4 = SVC(kernel='rbf')
clf4.fit(X_train, y_train)
print("Classifier 4 accuracy: ",
accuracy_score(clf4.predict(X_test), y_test))
X_train, X_test, y_train, y_test = train_test_split(X_t5,
y_t5,
test_size=.30,
random_state=42)
clf5 = SVC(kernel='rbf')
clf5.fit(X_train, y_train)
print("Classifier 5 accuracy: ",
accuracy_score(clf5.predict(X_test), y_test))
X_train, X_test, y_train, y_test = train_test_split(X_t6,
y_t6,
test_size=.30,
random_state=52)
clf6 = SVC(kernel='rbf')
clf6.fit(X_train, y_train)
print("Classifier 6 accuracy: ",
accuracy_score(clf6.predict(X_test), y_test))
X_train, X_test, y_train, y_test = train_test_split(X_t7,
y_t7,
test_size=.30,
random_state=21)
clf7 = SVC(kernel='rbf')
clf7.fit(X_train, y_train)
print("Classifier 7 accuracy: ",
accuracy_score(clf7.predict(X_test), y_test))
X_train, X_test, y_train, y_test = train_test_split(X_t8,
y_t8,
test_size=.30,
random_state=73)
clf8 = SVC(kernel='rbf')
clf8.fit(X_train, y_train)
print("Classifier 8 accuracy: ",
accuracy_score(clf8.predict(X_test), y_test))
#================================================================================================
raw_features = extract.start(file_name)
raw_baseline_angle = raw_features[0]
baseline_angle, comment = categorize.determine_baseline_angle(
raw_baseline_angle)
result["Baseline Angle"] = comment
raw_top_margin = raw_features[1]
top_margin, comment = categorize.determine_top_margin(raw_top_margin)
result["Top Margin"] = comment
raw_letter_size = raw_features[2]
letter_size, comment = categorize.determine_letter_size(raw_letter_size)
result["Letter Size"] = comment
raw_line_spacing = raw_features[3]
line_spacing, comment = categorize.determine_line_spacing(raw_line_spacing)
result["Line Spacing"] = comment
raw_word_spacing = raw_features[4]
word_spacing, comment = categorize.determine_word_spacing(raw_word_spacing)
result["Word Spacing"] = comment
raw_pen_pressure = raw_features[5]
pen_pressure, comment = categorize.determine_pen_pressure(raw_pen_pressure)
result["Pen Pressure"] = comment
raw_slant_angle = raw_features[6]
slant_angle, comment = categorize.determine_slant_angle(raw_slant_angle)
result["Slant"] = comment
result["Emotional Stability"] = clf1.predict(
[[baseline_angle, slant_angle]])
result["Mental Energy or Will Power"] = clf2.predict(
[[letter_size, pen_pressure]])
result["Modesty"] = clf3.predict([[letter_size, top_margin]])
result["Personal Harmony and Flexibility"] = clf4.predict(
[[line_spacing, word_spacing]])
result["Lack of Discipline"] = clf5.predict([[slant_angle, top_margin]])
result["Poor Concentration"] = clf6.predict([[letter_size, line_spacing]])
result["Non Communicativeness"] = clf7.predict(
[[letter_size, word_spacing]])
result["Social Isolation"] = clf8.predict([[line_spacing, word_spacing]])
return result
def identifyPersonalityTraits(file_name_orig):
clf_emotional_stability = load('knowme_EmotionalSt.joblib')
clf_knowme_MentalE_WlPower = load('knowme_MentalE_WlPower.joblib')
clf_knowme_Modesty = load('knowme_Modesty.joblib')
clf_lackOfDiscipline = load('lackOfDiscipline.joblib')
clf_PoorConcentration = load('PoorConcentration.joblib')
clf_SocialIsolation = load('SocialIsolation.joblib')
# file_name_orig ="Michael_HW.png"
# crop(file_name_orig)
file_name = resize(file_name_orig)
raw_features = extract.start(file_name)
raw_baseline_angle = raw_features[0]
baseline_angle, comment = categorize.determine_baseline_angle(
raw_baseline_angle)
print("Baseline Angle: " + comment)
raw_top_margin = raw_features[1]
top_margin, comment = categorize.determine_top_margin(raw_top_margin)
print("Top Margin: " + comment)
raw_letter_size = raw_features[2]
letter_size, comment = categorize.determine_letter_size(raw_letter_size)
print("Letter Size: " + comment)
raw_line_spacing = raw_features[3]
line_spacing, comment = categorize.determine_line_spacing(raw_line_spacing)
print("Line Spacing: " + comment)
raw_word_spacing = raw_features[4]
word_spacing, comment = categorize.determine_word_spacing(raw_word_spacing)
print("Word Spacing: " + comment)
raw_pen_pressure = raw_features[5]
pen_pressure, comment = categorize.determine_pen_pressure(raw_pen_pressure)
print("Pen Pressure: " + comment)
raw_slant_angle = raw_features[6]
slant_angle, comment = categorize.determine_slant_angle(raw_slant_angle)
print("Slant: " + comment)
emotional_stability = clf_emotional_stability.predict(
[[baseline_angle, slant_angle]])
MentalE_WlPower = clf_knowme_MentalE_WlPower.predict(
[[letter_size, pen_pressure]])
Modesty = clf_knowme_Modesty.predict([[letter_size, top_margin]])
Discipline = clf_lackOfDiscipline.predict([[slant_angle, top_margin]])
Concentration = clf_PoorConcentration.predict([[letter_size,
line_spacing]])
SocialIsolation = clf_SocialIsolation.predict(
[[line_spacing, word_spacing]])
if (emotional_stability[0] == 1):
emotional_stability = "Stable"
else:
emotional_stability = "Not Stable"
if (MentalE_WlPower[0] == 1):
MentalE_WlPower = "High or Average"
else:
MentalE_WlPower = "Low"
if (Modesty[0] == 1):
modesty = "Observed"
else:
modesty = "Not Observed"
if (Concentration[0] == 1):
concentration = "Observed"
else:
concentration = "Not Observed"
if (Discipline[0] == 1):
discipline = "Observed"
else:
discipline = "Not Observed"
if (SocialIsolation[0] == 1):
SocialIsolation = "Observed"
else:
SocialIsolation = "Not Observed"
personality_Trait_dict = {
"Emotional_Stability": emotional_stability,
"Mental_Power": MentalE_WlPower,
"Modesty": modesty,
"Discipline": discipline,
"Concentration": concentration,
"Social_Isolation": SocialIsolation
}
print(personality_Trait_dict)
return personality_Trait_dict
def predict(fname):
X_baseline_angle = []
X_top_margin = []
X_letter_size = []
X_line_spacing = []
X_word_spacing = []
X_pen_pressure = []
X_slant_angle = []
y_t1 = []
y_t2 = []
y_t3 = []
y_t4 = []
y_t5 = []
y_t6 = []
y_t7 = []
y_t8 = []
page_ids = []
if os.path.isfile("prediction\label_list"):
print("Info: label_list found.")
#=================================================================
with open("prediction\label_list", "r") as labels:
for line in labels:
content = line.split()
baseline_angle = float(content[0])
X_baseline_angle.append(baseline_angle)
top_margin = float(content[1])
X_top_margin.append(top_margin)
letter_size = float(content[2])
X_letter_size.append(letter_size)
line_spacing = float(content[3])
X_line_spacing.append(line_spacing)
word_spacing = float(content[4])
X_word_spacing.append(word_spacing)
pen_pressure = float(content[5])
X_pen_pressure.append(pen_pressure)
slant_angle = float(content[6])
X_slant_angle.append(slant_angle)
trait_1 = float(content[7])
y_t1.append(trait_1)
trait_2 = float(content[8])
y_t2.append(trait_2)
trait_3 = float(content[9])
y_t3.append(trait_3)
trait_4 = float(content[10])
y_t4.append(trait_4)
trait_5 = float(content[11])
y_t5.append(trait_5)
trait_6 = float(content[12])
y_t6.append(trait_6)
trait_7 = float(content[13])
y_t7.append(trait_7)
trait_8 = float(content[14])
y_t8.append(trait_8)
page_id = content[15]
page_ids.append(page_id)
#===============================================================
# emotional stability
X_t1 = []
for a, b in itertools.izip(X_baseline_angle, X_slant_angle):
X_t1.append([a, b])
# mental energy or will power
X_t2 = []
for a, b in itertools.izip(X_letter_size, X_pen_pressure):
X_t2.append([a, b])
# modesty
X_t3 = []
for a, b in itertools.izip(X_letter_size, X_top_margin):
X_t3.append([a, b])
# personal harmony and flexibility
X_t4 = []
for a, b in itertools.izip(X_line_spacing, X_word_spacing):
X_t4.append([a, b])
# lack of discipline
X_t5 = []
for a, b in itertools.izip(X_slant_angle, X_top_margin):
X_t5.append([a, b])
# poor concentration
X_t6 = []
for a, b in itertools.izip(X_letter_size, X_line_spacing):
X_t6.append([a, b])
# non communicativeness
X_t7 = []
for a, b in itertools.izip(X_letter_size, X_word_spacing):
X_t7.append([a, b])
# social isolation
X_t8 = []
for a, b in itertools.izip(X_line_spacing, X_word_spacing):
X_t8.append([a, b])
#print X_t1
#print type(X_t1)
#print len(X_t1)
X_train, X_test, y_train, y_test = train_test_split(X_t1,
y_t1,
test_size=.30,
random_state=8)
clf1 = SVC(kernel='rbf')
clf1.fit(X_train, y_train)
print "Classifier 1 accuracy: ", accuracy_score(
clf1.predict(X_test), y_test)
X_train, X_test, y_train, y_test = train_test_split(X_t2,
y_t2,
test_size=.30,
random_state=16)
clf2 = SVC(kernel='rbf')
clf2.fit(X_train, y_train)
print "Classifier 2 accuracy: ", accuracy_score(
clf2.predict(X_test), y_test)
X_train, X_test, y_train, y_test = train_test_split(X_t3,
y_t3,
test_size=.30,
random_state=32)
clf3 = SVC(kernel='rbf')
clf3.fit(X_train, y_train)
print "Classifier 3 accuracy: ", accuracy_score(
clf3.predict(X_test), y_test)
X_train, X_test, y_train, y_test = train_test_split(X_t4,
y_t4,
test_size=.30,
random_state=64)
clf4 = SVC(kernel='rbf')
clf4.fit(X_train, y_train)
print "Classifier 4 accuracy: ", accuracy_score(
clf4.predict(X_test), y_test)
X_train, X_test, y_train, y_test = train_test_split(X_t5,
y_t5,
test_size=.30,
random_state=42)
clf5 = SVC(kernel='rbf')
clf5.fit(X_train, y_train)
print "Classifier 5 accuracy: ", accuracy_score(
clf5.predict(X_test), y_test)
X_train, X_test, y_train, y_test = train_test_split(X_t6,
y_t6,
test_size=.30,
random_state=52)
clf6 = SVC(kernel='rbf')
clf6.fit(X_train, y_train)
print "Classifier 6 accuracy: ", accuracy_score(
clf6.predict(X_test), y_test)
X_train, X_test, y_train, y_test = train_test_split(X_t7,
y_t7,
test_size=.30,
random_state=21)
clf7 = SVC(kernel='rbf')
clf7.fit(X_train, y_train)
print "Classifier 7 accuracy: ", accuracy_score(
clf7.predict(X_test), y_test)
X_train, X_test, y_train, y_test = train_test_split(X_t8,
y_t8,
test_size=.30,
random_state=73)
clf8 = SVC(kernel='rbf')
clf8.fit(X_train, y_train)
print "Classifier 8 accuracy: ", accuracy_score(
clf8.predict(X_test), y_test)
#================================================================================================
while True:
#file_name = raw_input("Enter file name to predict or z to exit: ")
#if file_name == 'z':
# break
raw_features = extract.start(fname)
comm = {}
raw_baseline_angle = raw_features[0]
baseline_angle, comment = categorize.determine_baseline_angle(
raw_baseline_angle)
print "Baseline Angle: " + comment
comm["Baseline_Angle"] = comment
raw_top_margin = raw_features[1]
top_margin, comment = categorize.determine_top_margin(
raw_top_margin)
print "Top Margin: " + comment
comm["Top_margin"] = comment
raw_letter_size = raw_features[2]
letter_size, comment = categorize.determine_letter_size(
raw_letter_size)
print "Letter Size: " + comment
comm["Letter_Size"] = comment
raw_line_spacing = raw_features[3]
line_spacing, comment = categorize.determine_line_spacing(
raw_line_spacing)
print "Line Spacing: " + comment
comm["Line_Spacing"] = comment
raw_word_spacing = raw_features[4]
word_spacing, comment = categorize.determine_word_spacing(
raw_word_spacing)
print "Word Spacing: " + comment
comm["Word_Spacing"] = comment
raw_pen_pressure = raw_features[5]
pen_pressure, comment = categorize.determine_pen_pressure(
raw_pen_pressure)
print "Pen Pressure: " + comment
comm["Pen_Pressure"] = comment
raw_slant_angle = raw_features[6]
slant_angle, comment = categorize.determine_slant_angle(
raw_slant_angle)
print "Slant: " + comment
comm["Slant"] = comment
Emotional_stability = clf1.predict([[baseline_angle,
slant_angle]])[0]
comm["Emotional_Stability"] = Emotional_stability
Mental_energy = clf2.predict([[letter_size, pen_pressure]])[0]
comm["Mental_Energy_or_Will_power"] = Mental_energy
Modesty = clf3.predict([[letter_size, top_margin]])[0]
comm["Modesty"] = Modesty
Harmony = clf3.predict([[letter_size, top_margin]])[0]
comm["Personal_Harmony_or_Will_Power"] = Harmony
lack_discipline = clf5.predict([[slant_angle, top_margin]])[0]
comm["Lack_of_Discipline"] = lack_discipline
poor_concentration = clf6.predict([[letter_size, line_spacing]])[0]
comm["Poor_Concentration"] = poor_concentration
Non_communicativeness = clf7.predict([[letter_size,
word_spacing]])[0]
comm["Non_Communicativeness"] = Non_communicativeness
Social_isolation = clf8.predict([[line_spacing, word_spacing]])[0]
comm["Social_Isolation"] = Social_isolation
print
print "Emotional Stability: ", Emotional_stability
print "Mental Energy or Will Power: ", Mental_energy
print "Modesty: ", Modesty
print "Personal Harmony and Flexibility: ", Harmony
print "Lack of Discipline: ", lack_discipline
print "Poor Concentration: ", poor_concentration
print "Non Communicativeness: ", Non_communicativeness
print "Social Isolation: ", Social_isolation
print "---------------------------------------------------"
print
break
#=================================================================================================
else:
print("Error: label_list file not found.")
return comm