Eda*_*Eda 5 python matrix pca dimensionality-reduction deep-learning
我想使用 pca 降维来降低大小为 (5,3844) 的矩阵的维数。I got this error n_components=1000 must be between 0 and min(n_samples, n_features)=2 with svd_solver='full'.
我尝试了超过 5 周的时间来寻找如何做到这一点。
任何帮助，将不胜感激。
coeffs = wavedec(y, 'sym5', level=5)
cA5,cD5,cD4,cD3,cD2,cD1=coeffs
result = []
common_x = np.linspace(0,3844, len(cD1))
for c in [cD5,cD4,cD3,cD2,cD1]:
x = np.linspace(0, 3844, len(c))
f = interp1d(x, c)
result.append(f(common_x)) #(5,3844)
pca = PCA(n_components=1000)
pca.fit(result)
data_pca = pca.transform(result)
print("original shape: ", result.shape) ##(5,3844)
print("transformed shape:", data_pca.shape)