错误如下：
ValueError: With n_samples=0, test_size=0.25 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.
代码如下：
# import the necessary packages
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from pyimagesearch.preprocessing import SimplePreprocessor
from pyimagesearch.datasets import SimpleDatasetLoader
from imutils import paths
import argparse
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--datasets", type=str, default="H:\111\try1\pyimagesearch\datasets\animals\",
help="path to input dataset")
ap.add_argument("-k", "--neighbors", type=int, default=1,
help="# of nearest neighbors for classification")
ap.add_argument("-j", "--jobs", type=int, default=-1,
help="# of jobs for k-NN distance (-1 uses all available cores)")
args = vars(ap.parse_args())
# grab the list of images that we’ll be describing
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["datasets"]))
# initialize the image preprocessor, load the dataset from disk,
# and reshape the data matrix
sp = SimplePreprocessor.SimplePreprocessor(32, 32)
sdl = SimpleDatasetLoader.SimpleDatasetLoader(preprocessors=[sp])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.reshape((data.shape[0], 3072))
# show some information on memory consumption of the images
print("[INFO] features matrix: {:.1f}MB".format(
data.nbytes / (1024 * 1000.0)))
encode the labels as integers
le = LabelEncoder()
labels = le.fit_transform(labels)
partition the data into training and testing splits using 75% of
the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data, labels,
test_size=0.25, random_state=42)
# train and evaluate a k-NN classifier on the raw pixel intensities
print("[INFO] evaluating k-NN classifier...")
model = KNeighborsClassifier(n_neighbors=args["neighbors"],
n_jobs=args["jobs"])
model.fit(trainX, trainY)
print(classification_report(testY, model.predict(testX),
target_names=le.classes_))
