Lines Matching refs:samples
12 def Relax(samples, iterations=10):
19 for i in xrange(1, len(samples)):
20 voronoi_boundaries.append((samples[i] + samples[i-1]) * 0.5)
23 relaxed_samples.append(samples[0])
24 for i in xrange(1, len(samples)-1):
27 relaxed_samples.append(samples[-1])
28 samples = relaxed_samples
29 return samples
32 samples = []
34 samples.append(position)
37 samples.append(position)
38 return samples
43 samples = []
44 normalized_samples, scale = statistics.NormalizeSamples(samples)
48 samples = [0.0, 0.0]
49 normalized_samples, scale = statistics.NormalizeSamples(samples)
53 samples = [0.0, 1.0/3.0, 2.0/3.0, 1.0]
54 normalized_samples, scale = statistics.NormalizeSamples(samples)
58 samples = [1.0/8.0, 3.0/8.0, 5.0/8.0, 7.0/8.0]
59 normalized_samples, scale = statistics.NormalizeSamples(samples)
60 self.assertEquals(normalized_samples, samples)
64 """Tests NormalizeSamples and Discrepancy with random samples.
66 Generates 10 sets of 10 random samples, computes the discrepancy,
67 relaxes the samples using Llloyd's algorithm in 1D, and computes the
68 discrepancy of the relaxed samples. Discrepancy of the relaxed samples
69 must be less than or equal to the discrepancy of the original samples.
73 samples = CreateRandomSamples(10)
74 samples = statistics.NormalizeSamples(samples)[0]
75 d = statistics.Discrepancy(samples)
76 relaxed_samples = Relax(samples)
82 samples = []
83 d = statistics.Discrepancy(samples)
86 samples = [0.5]
87 d = statistics.Discrepancy(samples)
90 samples = [0.0, 1.0]
91 d = statistics.Discrepancy(samples)
94 samples = [0.5, 0.5, 0.5]
95 d = statistics.Discrepancy(samples)
98 samples = [1.0/8.0, 3.0/8.0, 5.0/8.0, 7.0/8.0]
99 d = statistics.Discrepancy(samples)
102 samples = [1.0/8.0, 5.0/8.0, 5.0/8.0, 7.0/8.0]
103 d = statistics.Discrepancy(samples)
106 samples = [1.0/8.0, 3.0/8.0, 5.0/8.0, 5.0/8.0, 7.0/8.0]
107 d = statistics.Discrepancy(samples)
110 samples = [0.0, 1.0/3.0, 2.0/3.0, 1.0]
111 d = statistics.Discrepancy(samples)
114 samples = statistics.NormalizeSamples(samples)[0]
115 d = statistics.Discrepancy(samples)
147 samples = [[0.0, 1.2, 2.3, 3.3], [6.3, 7.5, 8.4], [4.2, 5.4, 5.9]]
148 d_0 = statistics.TimestampsDiscrepancy(samples[0])
149 d_1 = statistics.TimestampsDiscrepancy(samples[1])
150 d_2 = statistics.TimestampsDiscrepancy(samples[2])
151 d = statistics.TimestampsDiscrepancy(samples)
160 samples = CreateRandomSamples(10)
161 samples = statistics.NormalizeSamples(samples)[0]
162 d = statistics.Discrepancy(samples)
163 d_approx = statistics.Discrepancy(samples, 500)