from typing import Union

import numpy as np
import pandas as pd
from sklearn.neighbors import BallTree


def hopkins(data_frame: Union[np.ndarray, pd.DataFrame], sampling_size: int) -> float:
    """Assess the clusterability of a dataset. A score between 0 and 1, a score around 0.5 express
    no clusterability and a score tending to 0 express a high cluster tendency.
    Examples
    --------
    >>> from sklearn import datasets
    >>> from pyclustertend import hopkins
    >>> X = datasets.load_iris().data
    >>> hopkins(X,150)
    0.16
    """

    if type(data_frame) == np.ndarray:
        data_frame = pd.DataFrame(data_frame)

    data_frame_sample = sample_observation_from_dataset(data_frame, sampling_size)

    sample_distances_to_nearest_neighbours = get_distance_sample_to_nearest_neighbours(
        data_frame, data_frame_sample
    )

    uniformly_selected_observations_df = simulate_df_with_same_variation(
        data_frame, sampling_size
    )

    df_distances_to_nearest_neighbours = get_nearest_sample(
        data_frame, uniformly_selected_observations_df
    )

    x = sum(sample_distances_to_nearest_neighbours)
    y = sum(df_distances_to_nearest_neighbours)

    if x + y == 0:
        raise Exception("The denominator of the hopkins statistics is null")

    return x / (x + y)[0]


def get_nearest_sample(df: pd.DataFrame, uniformly_selected_observations: pd.DataFrame):
    tree = BallTree(df, leaf_size=2)
    dist, _ = tree.query(uniformly_selected_observations, k=1)
    uniformly_df_distances_to_nearest_neighbours = dist
    return uniformly_df_distances_to_nearest_neighbours


def simulate_df_with_same_variation(
    df: pd.DataFrame, sampling_size: int
) -> pd.DataFrame:
    
    min_data_frame = df.min()
    max_data_frame = df.max()

    obs_all = []
    for min, max in zip(min_data_frame, max_data_frame):
        obs = np.random.uniform(min, max, sampling_size)
        obs_all.append(obs)
    
    res = pd.DataFrame(obs_all).T
    return res


def get_distance_sample_to_nearest_neighbours(df: pd.DataFrame, data_frame_sample):
    tree = BallTree(df, leaf_size=2)
    dist, _ = tree.query(data_frame_sample, k=2)
    data_frame_sample_distances_to_nearest_neighbours = dist[:, 1]
    return data_frame_sample_distances_to_nearest_neighbours


def sample_observation_from_dataset(df, sampling_size: int):
    if sampling_size > df.shape[0]:
        raise Exception("The number of sample of sample is bigger than the shape of D")
    data_frame_sample = df.sample(n=sampling_size)
    return data_frame_sample