# from tsne import bh_sne # thi is the correct tsne to use. It's the one discussed btnw

# from bhtsne import bh_tsne

import sklearn.manifold

from matplotlib import pyplot as plt

import pandas

import scipy

import numpy as np

import os

import gc

from time import gmtime, strftime

import seaborn as sns

from os import listdir

from os.path import isfile, join

import math

from scipy.stats.stats import pearsonr

import random as rand

from sklearn.preprocessing import normalize

from collections import defaultdict

def memo(f):

return helper

def zero_to_one(array):

return result

# @memo

def load_dataset(path):

return result.sort(['hcad']).fillna(0)

def fast_tsne(df_data, dest_folder="", n = None, file_tag= "", embedded_dimensions=2, perplexity = 50):

return embedding

def slow_tsne(df_data, dest_folder, n = None, file_tag= "", embedded_dimensions=2, perplexity = 50):

return embedding

def hist_2d(vis_x,vis_y):

plt.colorbar()

def pairwise_plot(pddf, sqrt = False):

plt.show()

def old_fast_show_ratio_plot(xy_points, y_data, log = False, normalize_buckets=True):

return np.nan_to_num(H)

#===============================================================================

def colored_scatter(xy_points, y_data):

plt.show()

def shuffle_in_unison(a, b):

numpy.random.shuffle(b)

def train_test_split(x_mat_in, y_col_in):

return X_train, y_train, X_val, y_val, X_test, y_test

def load_mega_hcad():

return train_test_split(mega_hcad, y_data_np[:, y_column])

print ("helpers executed")