diff --git a/ex-1/kde-plot.py b/ex-1/kde-plot.py
new file mode 100755
index 0000000..9cec239
--- /dev/null
+++ b/ex-1/kde-plot.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from scipy.stats    import moyal
+from scipy.optimize import minimize
+
+
+def gaussian_kde(dataset, points):
+    n = dataset.size
+    m = points.size
+
+    var = np.var(dataset) * np.power(n*3/4, -2/5) * 0.4
+    result = np.zeros(m)
+
+    if m >= n:
+        for i in range(n):
+            diff = dataset[i] - points
+            result += np.exp(-diff**2/(2*var))/n
+    else:
+        for i in range(m):
+            diff = dataset - points[i]
+            result[i] = np.sum(np.exp(-diff**2 / (2*var)))
+
+    return result / np.sqrt(2*np.pi*var) / n
+
+
+def gaussian_kde_point(dataset, var, x):
+    var *= np.power(dataset.size * 3/4, -2/5) * 0.4
+    val = np.sum(np.exp(-(x - dataset)**2 / (2*var))) / dataset.size
+    return val / np.sqrt(2*np.pi*var)
+
+
+def hsm(x):
+    n = len(x)
+    while n > 3:
+        k = n//2
+        diffs = x[k:] - x[:-k]
+
+        i = np.where(diffs == np.min(diffs))[0]
+        i = int(np.mean(i))
+
+        if diffs[i] == 0:
+            return x[i]
+        else:
+            x = x[i:i+k]
+        n = len(x)
+
+    if n == 3:
+        d = 2*x[1] - x[0] - x[2]
+        return np.mean(x[1:] if d > 0 else x[:2])
+
+    if n < 3:
+        return np.mean(x)
+
+
+def bootstrap(f, x, n):
+    vals = np.empty(n)
+    for i in range(n):
+        s = np.random.choice(x, size=x.size)
+        vals[i] = f(np.sort(s))
+    return vals
+
+
+np.random.seed(1)
+x = np.linspace(-2, 25, 400)
+sample = moyal.rvs(size=5000, loc=4)
+
+s = np.argsort(sample)
+
+f = gaussian_kde(sample, x)
+
+
+def kde(sample):
+    var = np.var(sample)
+    f = lambda x: -gaussian_kde_point(sample, var, x)
+    M = minimize(lambda x: f(x), hsm(sample)).x
+    return M
+
+m = bootstrap(hsm, sample, 500)
+M = bootstrap(kde, sample, 500)
+
+print(m.mean(), m.std())
+print(M.mean(), M.std())
+
+plt.figure()
+plt.plot(x, moyal.pdf(x, loc=4), c='xkcd:gray', label='true PDF')
+plt.plot(x, f, '#92182b', label='empirical PDF')
+plt.plot(sample, np.zeros_like(sample),
+         '|', c='xkcd:navy')
+#plt.axvline(x=m.mean(), c='r')
+plt.axvline(x=M.mean(), c='xkcd:camel', label='mode')
+plt.legend()
+plt.show()