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persolhs.py

+import numpy as np
+import matplotlib.pyplot as plt
+
+#makes a Latin Hyper Cube sample
+#returns a matrix X of size n by p
+#of a LHS of n values on each of p variables
+#for each column of X, the n values are randomly
+#distributed with one from each interval
+#(0,1/n), (1/n,2/n), ..., (1-1/n,1)
+#and they are randomly permuted
+
+#def lhssample(n=10,p=2):
+
+#ça donne des résultats moyen beau il y a possiblement une histoire de couches de points à rajouter
+def lhssample(n,p):
+    x = np.random.uniform(size=[n,p])
+    for i in range(0,p):
+        x[:,i] = (np.argsort(x[:,i])+0.5)/n
+    return x
+
+x = lhssample(10,10)
+
+
+print(x.shape)
+
+
+plt.plot(x[:, 0], x[:, 1], "o")
+plt.xlabel("x")
+plt.ylabel("y")
+plt.grid()
+plt.show()
+