Which is the algorithm for knn density estimator? I am reading Pattern Recognition and Machine Learning by Christopher Bishop. In chapter two he talk about using knn to density estimation. I want to replicate a plot using python/R/matlab.  He is doing it with synthetic data, but I do not know how to update the value V (volume of Region containing X from p(x)) in the following formula $$P(x)=\frac{K}{NV}$$. I could not find any implementation of this algorithm for density estimation.
This is the plot:

 A: If your data lives in $p$-dimensional space, then $V = V_p(x)$ is the volume of a $p$-dimensional ball with radius equal to the distance of $x$ from its $k$-th nearest neighbour. So assume $x_k$ is the $k$-th nearest neighbour of $x$, then
\begin{align*}
p_k(x)  =\frac{k}{n} \frac{1}{ \frac{\pi^{p/2}}{\Gamma(p/2+1)}  \|x-x_k \|}.
\end{align*}
Here is an example taken from these notes: Assume $p=1$, and we have data $\mathcal{X} = \{1,2,6,11,13,14,20,33 \}$, and we wish to find the knn density estimator at $x=5$ and $k=2$. The distance from $x=5$ to each data point is
$$
\{4,3,1,6,8,9,15,28 \}
$$
So its nearest neighbour is 6, and it's second nearest neighbour is 2, which is a distance of 3 away. Then we have
$$
p_{k}(x) = \frac{2}{8} \frac{1}{ \frac{\pi^{1/2}}{\Gamma(3/2)} \times 3} = \frac{1}{24}.
$$
Here is a (naive) Python implementation where I sample randomly from a Gaussian and then build the knn density estimator on top of that sample for varying k, producing the following plot:

The code used is:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

gaussian = norm(loc=0.5, scale=0.2)
X = gaussian.rvs(500)
grid = np.linspace(-0.1, 1.1, 1000)

Ks = [4,10,60]
fig, axes = plt.subplots(3,1, figsize=(10,10))

for i, ax in enumerate(axes.flat):
    
    # choose K value
    K = Ks[i]
    
    # run knn density estimation with chosen K
    p = np.zeros_like(grid)
    n = X.shape[0]
    for i, x in enumerate(grid):
        dists = np.abs(X-x)
        neighbours = dists.argsort()
        neighbour_K = neighbours[K]
        p[i] = (K/n) * 1/(2 * dists[neighbour_K])
        
    # True dist
    ax.plot(grid, gaussian.pdf(grid))
    
    # plot density estimate
    ax.plot(grid, p)
    
    ax.set_title(f'$k={K}$')
plt.savefig("knn-density_est.png", dpi=300)
plt.show()

