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在我解釋我的情況之前，我想告訴我這個代碼不是我的，我僅用作實驗目的的參考。這些代碼屬於合法的owner。Python IOError：[Errno 2]沒有這樣的文件或目錄
我正在試驗機器學習。我使用這段代碼來獲得關於單次學習的想法。
import numpy as np
import copy
from scipy.ndimage import imread
from scipy.spatial.distance import cdist
nrun = 20
fname_label = 'class_labels.txt'
def LIAP(fn):
I = imread(fn, flatten=True)
I = np.array(I, dtype=bool)
I = np.logical_not(I)
(row, col) = I.nonzero()
D = np.array(row, col)
D = np.transpose(D)
D = D.astype(float)
D = D.shape[0]
mean = np.mean(D, axis=0)
for i in mean(D, axis=0):
D[i, :] = D[i, :] - mean
return D
def MHD(itemA, itemB):
D = cdist(itemA, itemB)
mindist_A = D.min(axis=1)
mindist_B = D.min(axis=0)
mean_A = np.mean(mindist_A)
mean_B = np.mean(mindist_B)
return max(mean_A, mean_B)
def classification_run(folder, f_load, f_cost, ftype='cost'):
assert ((ftype == 'cost') | (ftype == 'score'))
with open(folder+'/'+fname_label) as f:
content = f.read().splitlines()
pairs = (line.split() for line in content)
test_files = [pair[0] for pair in pairs]
train_files = [pair[1] for pair in pairs]
answers_files = copy.copy(train_files)
test_files.sort()
train_files.sort()
ntrain = len(train_files)
ntest = len(test_files)
train_items = [f_load(f) for f in train_files]
test_items = [f_load(f) for f in test_files]
costM = np.zeros((ntest, ntrain), float)
for i in range(ntest):
for c in range(ntrain):
costM[i, c] = f_cost(test_items[i], train_items[c])
if ftype == 'cost':
YHAT = np.argmin(costM, axis=1)
elif ftype == 'score':
YHAT = np.argmax(costM, axis=1)
else:
assert False
correct = 0.0
for i in range(ntest):
if train_files[YHAT[i]] == answers_files[i]:
correct += 1.0
pcorrect = 100 * correct/ntest
perror = 100 - pcorrect
return perror
if __name__ == "__main__":
print 'One-shot classification demo with Modified Hausdorff Distance'
perror = np.zeros(nrun)
for r in range(1, nrun+1):
rs = str(r)
if len(rs) == 1:
rs = '0' + rs
perror[r-1] = classification_run('run'+rs, LIAP, MHD, 'cost')
print " run " + str(r) + " (error" + str(perror[r-1]) + "%)"
total = np.mean(perror)
print " average error" + str(total) + "%"
但顯然，我收到一個IOError。
One-shot classification demo with Modified Hausdorff Distance
Traceback (most recent call last):
File "demo.py", line 121, in <module>
'cost')
File "demo.py", line 31, in classification_run
with open(os.path.join(path_to_all_runs, folder, fname_label)) as f:
IOError: [Errno 2] No such file or directory: '/Users/gilangrilhami/Documents/MachineLearning/ml_projects/one/all_runs/run01/class_labels.txt'
據我所知，這應該使一個文件夾，每次運行，並創建它自己的「class_labels.txt」。我試圖閱讀評論部分，以防萬一我錯過了一些東西，任何人都有同樣的問題。但我找不到任何相關的東西。我想找到一個解決方案，或者我錯過了一些東西。
謝謝你的時間。