import json
import random
import numpy as np

raw_data = []

with open("features_3k.json") as file_json:
    raw_data = json.load(file_json)
    print(len(raw_data))

#  random.shuffle(data)
data = raw_data.copy()

def get_dangerous_pairs(thresh):
    for i in range(len(raw_data) - 1):
        test = np.array(raw_data[i]["feature"])
        #  test = test.reshape(test, (1, test.shape[0]))
        for j in range(i+1, len(raw_data)):
            train = np.array(raw_data[j]["feature"])
            dist = np.sqrt(np.sum(np.square(test-train)))
            if dist < thresh:
                print("Dangerous pairs:",
                      raw_data[i]["name"], '(ID: ',raw_data[i]["id"],')',
                      '-',
                      raw_data[j]["name"], '(ID: ',raw_data[j]["id"],')',
                      '(Dist: ',dist,')')
            #  train = train.reshape(train, (1, train.shape[0]))

def calculate_ratio(split_ratio, thresh_hold):
    #  split_ratio = 0.2
    #  thresh_hold = 0.7

    nTest = int (len(data) * split_ratio)
    nTrain = len(data) - nTest

    test_data = np.array([x["feature"] for x in data[:nTest]])
    train_data = np.array([x["feature"] for x in data[nTest:]])
    #  print(split_ratio, test_data.shape, train_data.shape)
    min_dists = []
    for i, row in enumerate(test_data):
        row = np.reshape(row, (1, row.shape[0]))
        repeat_row = np.tile(row, (nTrain, 1))

        dists = repeat_row - train_data
        dists = np.sqrt(np.sum(np.square(dists), axis=1))
        #  print(dists)
        min_dist_idx = np.argmin(dists)
        #  print(i, min_dist_idx, dists[min_dist_idx])
        min_dists.append(dists[min_dist_idx])

    min_dists = np.array(min_dists)
#  print(min_dists.shape)
#  print(min_dists[min_dists > 0.75].shape)
    t1 = min_dists[min_dists > thresh_hold].shape[0]
    t2 = min_dists.shape[0]
    print('\tthresh:', thresh_hold, '\tratio:', t1/t2, '(', t1, '/', t2 ,')')
    return t1/t2

def find_best_threshold():
    for j in range(10, 60, 5):
        split = j / 100
        print('\nSplit test/train:', split*10, '/', 10-split*10, '====================================')
        best_thresh = 0
        best_ratio = 0
        for i in range(50, 110, 2):
            thresh = i / 100
            ratio = calculate_ratio(split, thresh)
            if ratio >= best_ratio:
                best_ratio = ratio
                best_thresh =thresh
        print('\tBEST THRESH: ', best_thresh, 'with ratio ', best_ratio)

get_dangerous_pairs(0.8)
find_best_threshold()