python - 主成分分析不起作用

我正在尝试对包含图像的数据集进行主成分分析，但每当我想从sklearn.discovery模块应用pca.transform时，我都会不断收到此错误：*attributeerError:'pca'对象没有“mean”*属性。我知道这个错误是什么意思，但我不知道该怎么纠正它。我想你们中有些人知道如何解决这个问题。
谢谢你的帮助
我的代码：

from sklearn import svm
import numpy as np
import glob
import os
from PIL import Image
from sklearn.decomposition import PCA

image_dir1 = "C:\Users\private\Desktop\K FOLDER\private\train"
image_dir2 = "C:\Users\private\Desktop\K FOLDER\private\test1"
Standard_size = (300,200)
pca = PCA(n_components = 10)
file_open = lambda x,y: glob.glob(os.path.join(x,y))


def matrix_image(image_path):
    "opens image and converts it to a m*n matrix"
    image = Image.open(image_path)
    print("changing size from %s to %s" % (str(image.size), str(Standard_size)))
    image = image.resize(Standard_size)
    image = list(image.getdata())
    image = map(list,image)
    image = np.array(image)
    return image
def flatten_image(image):
    """
    takes in a n*m numpy array and flattens it to
    an array of the size (1,m*n)
    """
    s = image.shape[0] * image.shape[1]
    image_wide = image.reshape(1,s)
    return image_wide[0]

if __name__ == "__main__":
    train_images = file_open(image_dir1,"*.jpg")
    test_images = file_open(image_dir2,"*.jpg")
    train_set = []
    test_set = []

    "Loop over all images in files and modify them"
    train_set = [flatten_image(matrix_image(image)) for image in train_images]
    test_set = [flatten_image(matrix_image(image)) for image in test_images]
    train_set = np.array(train_set)
    test_set = np.array(test_set)
    train_set = pca.fit_transform(train_set) "line where error occurs"
    test_set = pca.fit_transform(test_set)

完全回溯：

Traceback (most recent call last):
  File "C:\Users\Private\workspace\final_submission\src\d.py", line 54, in <module>
    train_set = pca.transform(train_set)
  File "C:\Python27\lib\site-packages\sklearn\decomposition\pca.py", line 298, in transform
    if self.mean_ is not None:
AttributeError: 'PCA' object has no attribute 'mean_'

Eddi1:
所以在转换模型之前，我试着去适应它，现在我得到了一个更奇怪的错误。我查了一下，它涉及到f2py，一个将fortran移植到python的模块，它是numpy库的一部分。

File "C:\Users\Private\workspace\final_submission\src\d.py", line 54, in <module>
    pca.fit(train_set)
  File "C:\Python27\lib\site-packages\sklearn\decomposition\pca.py", line 200, in fit
    self._fit(X)
  File "C:\Python27\lib\site-packages\sklearn\decomposition\pca.py", line 249, in _fit
    U, S, V = linalg.svd(X, full_matrices=False)
  File "C:\Python27\lib\site-packages\scipy\linalg\decomp_svd.py", line 100, in svd
    full_matrices=full_matrices, overwrite_a = overwrite_a)
ValueError: failed to create intent(cache|hide)|optional array-- must have defined dimensions but got (0,)

编辑2：
所以我已经检查了我的火车组和数据组是否包含任何数据，但没有。
我已经检查了我的图像目录，它们包含正确的位置（为了清晰起见，我通过查看实际文件、查看其中一个图像的属性并复制位置来获取它们）。错误应该在别的地方。

最佳答案

在转换之前，您应该调整模型：

train_set = np.array(train_set)
test_set = np.array(test_set)

pca.fit(train_set)
pca.fit(test_set)

train_set = pca.transform(train_set) "line where error occurs"
test_set = pca.transform(test_set)

编辑
第二个错误表明您的train_set为空。使用此代码可以很容易地复制：

train_set = np.array([[]])
pca.fit(train_set)

我认为有一个问题在函数中。我可能错了，但这一行会提高flatten_image

image.wide = image.reshape(1,s)

可替换为：

image_wide = image.reshape(1,s)
return image_wide[0]

这一行也有问题：

print("changing size from %s to %s" % str(image.size), str(Standard_size))

有关更多详细信息，请阅读http://docs.python.org/2/library/stdtypes.html#string-formatting-operations，但AttributeError。所以你需要这样做：

print("changing size from %s to %s" % (str(image.size), str(Standard_size)))

另一个编辑
最后，将tvalues must be a tuple之后的循环替换为：

train_set = [flatten_image(matrix_image(image)) for image in train_images]
test_set = [flatten_image(matrix_image(image)) for image in test_images]

现在，您调用"Loop over all images in files and modify them"以便它在如下路径中查找文件：file_open并得到空列表而不是文件名。

关于python - 主成分分析不起作用，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/19410491/