我正在使用 Shogun Toolbox 的 Python 版本。我想使用LinearTimeMMD，它在流接口下接受数据CStreamingFeatures。我有两个RealFeatures对象形式的数据：feat_p和feat_q. 这些与QuadraticTimeMMD.
为了将它与 LinearTimeMMD 一起使用，我需要从这些创建对象 -据我所知，StreamingFeatures在这种情况下，这些将是。StreamingRealFeatures
我的第一种方法是使用这个：
gen_p, gen_q = StreamingRealFeatures(feat_p), StreamingRealFeatures(feat_q)
然而，这似乎不起作用：LinearTimeMMD 提供警告和不切实际的结果（随着样本数量不断增长）并调用gen_p.get_dim_feature_space()返回-1。此外，如果我尝试调用gen_p.get_streamed_features(100)它会导致内存访问错误。
我尝试了另一种方法StreamingFileFromFeatures：
streamFile_p = sg.StreamingFileFromRealFeatures()
streamFile_p.set_features(feat_p)
streamFile_q = sg.StreamingFileFromRealFeatures()
streamFile_q.set_features(feat_q)
gen_p = StreamingRealFeatures(streamFile_p, False, 100)
gen_q = StreamingRealFeatures(streamFile_q, False, 100)
但这会导致相同的情况和相同的描述问题。似乎在这两种情况下，都无法访问RealFeatures传递给对象的对象内容。StreamingRealFeatures我究竟做错了什么？
编辑：我被要求提供一个小的工作示例来显示错误：
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
from shogun import StreamingRealFeatures
import numpy as np
from matplotlib import pyplot as plt
from scipy.stats import laplace, norm
def sample_gaussian_vs_laplace(n=220, mu=0.0, sigma2=1, b=np.sqrt(0.5)):
# sample from both distributions
X=norm.rvs(size=n)*np.sqrt(sigma2)+mu
Y=laplace.rvs(size=n, loc=mu, scale=b)
return X,Y
# Main Script
mu=0.0
sigma2=1
b=np.sqrt(0.5)
n=220
X,Y=sample_gaussian_vs_laplace(n, mu, sigma2, b)
# turn data into Shogun representation (columns vectors)
feat_p=sg.RealFeatures(X.reshape(1,len(X)))
feat_q=sg.RealFeatures(Y.reshape(1,len(Y)))
gen_p, gen_q = StreamingRealFeatures(feat_p), StreamingRealFeatures(feat_q)
print("Dimensions: ", gen_p.get_dim_feature_space())
print("Number of features: ", gen_p.get_num_features())
print("Number of vectors: ", gen_p.get_num_vectors())
test_features = gen_p.get_streamed_features(1)
print("success")
编辑 2：工作示例的输出：
Dimensions: -1
Number of features: -1
Number of vectors: 1
Speicherzugriffsfehler (Speicherabzug geschrieben)
编辑 3：直接使用 RealFeatures 的带有 LinearTimeMMD 的附加代码。
mmd = sg.LinearTimeMMD()
kernel = sg.GaussianKernel(10, 1)
mmd.set_kernel(kernel)
mmd.set_p(feat_p)
mmd.set_q(feat_q)
mmd.set_num_samples_p(1000)
mmd.set_num_samples_q(1000)
alpha = 0.05
# Code taken from notebook example on
# http://www.shogun-toolbox.org/notebook/latest/mmd_two_sample_testing.html
# Location on page: In[16]
block_size=100
mmd.set_num_blocks_per_burst(block_size)
# compute an unbiased estimate in linear time
statistic=mmd.compute_statistic()
print("MMD_l[X,Y]^2=%.2f" % statistic)
编辑 4：显示日益严重的 mmd 问题的附加代码示例：
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
from shogun import StreamingRealFeatures
import numpy as np
from matplotlib import pyplot as plt
def mmd(n):
X = [(1.0,i) for i in range(n)]
Y = [(2.0,i) for i in range(n)]
X = np.array(X)
Y = np.array(Y)
# turn data into Shogun representation (columns vectors)
feat_p=sg.RealFeatures(X.reshape(2, len(X)))
feat_q=sg.RealFeatures(Y.reshape(2, len(Y)))
mmd = sg.LinearTimeMMD()
kernel = sg.GaussianKernel(10, 1)
mmd.set_kernel(kernel)
mmd.set_p(feat_p)
mmd.set_q(feat_q)
mmd.set_num_samples_p(100)
mmd.set_num_samples_q(100)
alpha = 0.05
block_size=100
mmd.set_num_blocks_per_burst(block_size)
# compute an unbiased estimate in linear time
statistic=mmd.compute_statistic()
print("N =", n)
print("MMD_l[X,Y]^2=%.2f" % statistic)
print()
for n in [1000, 10000, 15000, 20000, 25000, 30000]:
mmd(n)
输出：
N = 1000
MMD_l[X,Y]^2=-12.69
N = 10000
MMD_l[X,Y]^2=-40.14
N = 15000
MMD_l[X,Y]^2=-49.16
N = 20000
MMD_l[X,Y]^2=-56.77
N = 25000
MMD_l[X,Y]^2=-63.47
N = 30000
MMD_l[X,Y]^2=-69.52