import math
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, cross_val_predict
from sklearn.neighbors import KernelDensity
import matplotlib.pyplot as plt
import numpy as np

from data import LabelledCollection

scale = 100


import quapy as qp

negatives = np.random.normal(loc = 0.2, scale=0.2, size=20000)
negatives = np.asarray([x for x in negatives if 0 <= x <= 1])

positives = np.random.normal(loc = 0.75, scale=0.05, size=20000)
positives = np.asarray([x for x in positives if 0 <= x <= 1])

prev = 0.1
test = np.concatenate([
    negatives[:int(len(negatives)*(1-prev))],
    positives[:int(len(positives)*(prev))],
])


nbins = 30

plt.rcParams.update({'font.size': 7})

fig = plt.figure()
positions = np.asarray([2,1,0])
colors = ['r', 'g', 'b']


ax = fig.add_subplot(111, projection='3d')
ax.set_box_aspect((3, 1, 0.8))

for post, c, z in zip([test, positives, negatives], colors, positions):

    hist, bins = np.histogram(post, bins=np.linspace(0,1, nbins+1), density=True)
    xs = (bins[:-1] + bins[1:])/2

    ax.bar(xs, hist, width=1 / nbins, zs=z, zdir='y', color=c, ec=c, alpha=0.6)


ax.yaxis.set_ticks(positions)
ax.yaxis.set_ticklabels([' '*20+'Test distribution', ' '*20+'Positive distribution', ' '*20+'Negative distribution'])
# ax.xaxis.set_ticks([])
# ax.xaxis.set_ticklabels([], minor=True)
ax.zaxis.set_ticks([])
ax.zaxis.set_ticklabels([], minor=True)


#plt.figure(figsize=(10,6))
#plt.show()
plt.savefig('./histograms3d_CACM2023.pdf')