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_get_new_circle_positions.py
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_get_new_circle_positions.py
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import random, threading, webbrowser
import gensim, pickle, random
import gensim_helpers
import numpy as np
import sklearn
import os
import pandas as pd
import pickle
from gensim.corpora import Dictionary
from flask import Flask, render_template, request, json, Response, render_template_string, jsonify
from flask_classful import FlaskView,route
from _display import *
from _prepare import prepare, js_PCoA, PreparedData, _pcoa
from _topic_similarity_matrix import *
from os import path, walk
from gensim.models.keyedvectors import KeyedVectors
from scipy.spatial import procrustes
import json as js
def get_circle_positions(topic_similarity_matrix):
#Get new circle position regarding to proposed topic similarity matrix
new_circle_positions = dict()
for lambda_ in range(0, 101):
lambda_ = lambda_/100
matrix_cosine_distance = 1-topic_similarity_matrix[lambda_]
np.fill_diagonal(matrix_cosine_distance,0)
new_circle_positions[lambda_]=_pcoa(matrix_cosine_distance, n_components=2).tolist()
#Apply procrusteres
lambdas = list(new_circle_positions.keys())
standardized_matrix = dict()
disparity_values = dict()
original_a = new_circle_positions[0.0]
for i in range(len(lambdas)-1):
#print(lambdas[i], lambdas[i+1])
original_b = new_circle_positions[lambdas[i+1]]
mtx1, mtx2, disparity = procrustes(original_a, original_b)
disparity_values[lambdas[i]] = disparity
standardized_matrix[lambdas[i]] = mtx1.tolist()
original_a = mtx2
standardized_matrix[lambdas[len(lambdas)-1]] = mtx2.tolist()
disparity_values[lambdas[len(lambdas)-1]] = disparity
new_circle_positions = standardized_matrix
new_circle_positions= json.dumps(new_circle_positions)
return new_circle_positions
def get_circle_positions_from_old_matrix(old_circle_positions, topic_similarity_matrix):
data_keys = list(old_circle_positions.keys())
#get new positions from new matrix
new_circle_positions = dict()
for lambda_ in range(0, 101):
lambda_ = lambda_/100
matrix_cosine_distance = 1-topic_similarity_matrix[lambda_]
np.fill_diagonal(matrix_cosine_distance,0)
new_circle_positions[str(lambda_)]=_pcoa(matrix_cosine_distance, n_components=2).tolist()
#Apply procrusteres. Compare old positions with new positions
standardized_matrix = dict()
disparity_values = dict()
for i in range(len(data_keys)):
current_omega = data_keys[i]
original_a = np.array(old_circle_positions[current_omega])
original_b = np.array(new_circle_positions[current_omega])
mtx1, mtx2, disparity = procrustes(original_a, original_b)
disparity_values[current_omega] = disparity
standardized_matrix[current_omega] = mtx2.tolist()
new_circle_positions = standardized_matrix
new_circle_positions= json.dumps(new_circle_positions)
return new_circle_positions