def main():
print "use agesonly.csv to predict:"
agesonly_path = "[your agesonly.csv path]" # change to your agesonly.csv path
agesonly_rows = load_match_data.load_csv(agesonly_path, True)
offset1 = get_offset(agesonly_rows)
print nlclassify([27, 30], agesonly_rows, offset1)
print nlclassify([30, 27], agesonly_rows, offset1)
print "use scaled matchmaker.csv to predict:"
matchmaker_path = "[your matchmaker.csv path]" # change to your matchmaker.csv path
ls = file(matchmaker_path)
numerical_rows = preprocess_data.to_numerical(ls)
rescaled_data = preprocess_data.rescale_data(numerical_rows)
offset2 = get_offset(rescaled_data)
new_p1 = [28.0, -1, -1, 26.0, -1, 1, 2, 0.8] # man doesn't want children, women wants
new_p2 = [28.0, -1, 1, 26.0, -1, 1, 2, 0.8] # both want children
print nlclassify(new_p1, rescaled_data, offset2)
print nlclassify(new_p2, rescaled_data, offset2)
# Step2. In the terminal, type following code line by line
from svmutil import *
prob = svm_problem(matches, features)
pm_ages = svm_parameter('-t 2') # indicates using radial-basis function
m = svm_train(prob, pm_ages)
# [1] is the the ground truth, used for calculating the accuracy ; [28.0, 26.0] is the new point
p_labels, p_acc, p_vals = svm_predict([1], [[28.0, 26.0]], m)
# using matchmaker.csv
# Step1. the output into terminal, type "matches, features = [your output]"
matchmaker_path = '[your matchmaker.csv path]' # change to your matchmaker.csv path
ls = file(matchmaker_path)
numerical_rows = preprocess_data.to_numerical(ls)
rescaled_data = preprocess_data.rescale_data(numerical_rows)
matches = []
features = []
for r in rescaled_data:
matches.append(r.match)
features.append(r.data)
print matches,',',features
# Step2. In the terminal, type following code line by line
from svmutil import *
prob = svm_problem(matches, features)
pm_matchmaker = svm_parameter('-t 2') # indicates using radial-basis function
m = svm_train(prob, pm_matchmaker)
