file2_contents = file2.read().split('\n') f2contents = file2_contents[1].split(',') # index 0 contains feature headers data1 = [] data2 = [] for k in range(0, len(f1contents)): try: f1contents[k] = float(f1contents[k]) data1.append(f1contents[k]) except: pass for k in range(0, len(f2contents)): try: f2contents[k] = float(f2contents[k]) data2.append(f2contents[k]) except: pass indices = Distance.prune(data1, data2) #print("Euclidean: " + str(Distance.euclidean_distance(data1, data2, indices))) output.write(files[i] + "," + files[j] + "," + "euclidean" + "," + str(Distance.euclidean_distance(data1, data2, indices)) + "\n") #print("City: " + str(Distance.city_distance(data1, data2))) output.write(files[i] + "," + files[j] + "," + "city" + "," + str(Distance.city_distance(data1, data2)) + "\n") #print("Chebychev: " + str(Distance.chebychev_distance(data1, data2))) output.write(files[i] + "," + files[j] + "," + "chebychev" + "," + str(Distance.chebychev_distance(data1, data2)) + "\n") #print("Cosine: " + str(Distance.cosine_difference(data1, data2))) output.write(files[i] + "," + files[j] + "," + "cosine" + "," + str(Distance.cosine_difference(data1, data2)) + "\n") #print("Correlation: " + str(Distance.correlation_distance(data1, data2))) output.write(files[i] + "," + files[j] + "," + "correlation" + "," + str(Distance.correlation_distance(data1, data2)) + "\n")