所以我试图在python中构建一个自然学习处理器，我正在使用我在网上找到的一些代码，然后调整自己的东西。但现在，它只是不想工作。它一直在给我

ValueError: Found array with 0 sample(s) (shape=(0, 262)) while a minimum of 1 is required.

这是我的代码。我道歉，如果它是凌乱的我只是直接从互联网上复制它：

from collections import Counter

import pandas

from nltk.corpus import stopwords

import pandas as pd

import numpy

headlines = []

apps = pd.read_csv('DataUse.csv')

for e in apps['title_lower']:

headlines.append(e)

testdata = pd.read_csv('testdata.csv')

# Find all the unique words in the headlines.

unique_words = list(set(" ".join(headlines).split(" ")))

def make_matrix(headlines, vocab):

matrix = []

for headline in headlines:

# Count each word in the headline, and make a dictionary.

counter = Counter(headline)

# Turn the dictionary into a matrix row using the vocab.

row = [counter.get(w, 0) for w in vocab]

matrix.append(row)

df = pandas.DataFrame(matrix)

df.columns = unique_words

return df

print(make_matrix(headlines, unique_words))

import re

# Lowercase, then replace any non-letter, space, or digit character in the headlines.

new_headlines = [re.sub(r'[^\w\s\d]','',h.lower()) for h in headlines]

# Replace sequences of whitespace with a space character.

new_headlines = [re.sub("\s+", " ", h) for h in new_headlines]

unique_words = list(set(" ".join(new_headlines).split(" ")))

# We've reduced the number of columns in the matrix a bit.

print(make_matrix(new_headlines, unique_words))

stopwords = set(stopwords.words('english'))

stopwords = [re.sub(r'[^\w\s\d]','',s.lower()) for s in stopwords]

unique_words = list(set(" ".join(new_headlines).split(" ")))

# Remove stopwords from the vocabulary.

unique_words = [w for w in unique_words if w not in stopwords]

# We're down to 34 columns, which is way better!

print(make_matrix(new_headlines, unique_words))

##

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##

##

from sklearn.feature_extraction.text import CountVectorizer

# Construct a bag of words matrix.

# This will lowercase everything, and ignore all punctuation by default.

# It will also remove stop words.

vectorizer = CountVectorizer(lowercase=True, stop_words="english")

matrix = vectorizer.fit_transform(headlines)

# We created our bag of words matrix with far fewer commands.

print(matrix.todense())

# Let's apply the same method to all the headlines in all 100000 submissions.

# We'll also add the url of the submission to the end of the headline so we can take it into account.

full_matrix = vectorizer.fit_transform(apps['title_lower'])

print(full_matrix.shape)

##

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##

##

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import chi2

# Convert the upvotes variable to binary so it works with a chi-squared test.

col = apps["total_shares"].copy(deep=True)

col_mean = col.mean()

col[col < col_mean] = 0

col[(col > 0) & (col > col_mean)] = 1

print col

# Find the 1000 most informative columns

selector = SelectKBest(chi2, k='all')

selector.fit(full_matrix, col)

top_words = selector.get_support().nonzero()

# Pick only the most informative columns in the data.

chi_matrix = full_matrix[:,top_words[0]]

##

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import numpy as numpy

transform_functions = [

lambda x: len(x),

lambda x: x.count(" "),

lambda x: x.count("."),

lambda x: x.count("!"),

lambda x: x.count("?"),

lambda x: len(x) / (x.count(" ") + 1),

lambda x: x.count(" ") / (x.count(".") + 1),

lambda x: len(re.findall("\d", x)),

lambda x: len(re.findall("[A-Z]", x)),

]

# Apply each function and put the results into a list.

columns = []

for func in transform_functions:

columns.append(apps["title_lower"].apply(func))

# Convert the meta features to a numpy array.

meta = numpy.asarray(columns).T

##

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##

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##

features = numpy.hstack([chi_matrix.todense()])

from sklearn.linear_model import Ridge

import random

train_rows = 262

# Set a seed to get the same "random" shuffle every time.

random.seed(1)

# Shuffle the indices for the matrix.

indices = list(range(features.shape[0]))

random.shuffle(indices)

# Create train and test sets.

train = features[indices[:train_rows], :]

test = features[indices[train_rows:], :]

print test

train_upvotes = apps['total_shares'].iloc[indices[:train_rows]]

test_upvotes = apps['total_shares'].iloc[indices[train_rows:]]

train = numpy.nan_to_num(train)

print (test)

# Run the regression and generate predictions for the test set.

reg = Ridge(alpha=.1)

reg.fit(train, train_upvotes)

predictions = reg.predict(test)

##

##

##

##

##

### We're going to use mean absolute error as an error metric.

### Our error is about 13.6 upvotes, which means that, on average,

### our prediction is 13.6 upvotes away from the actual number of upvotes.

##print(sum(abs(predictions - test_upvotes)) / len(predictions))

##

### As a baseline, we'll use the average number of upvotes

### across all submissions.

### The error here is 17.2 -- our estimate is better, but not hugely so.

### There either isn't a ton of predictive value encoded in the

### data we have, or we aren't extracting it well.

##average_upvotes = sum(test_upvotes)/len(test_upvotes)

##print(sum(abs(average_upvotes - test_upvotes)) / len(predictions))

##

编辑：这是错误：

Traceback (most recent call last):

File "C:/Users/Tucker Siegel/Desktop/Machines/Test.py", line 156, in

predictions = reg.predict(test)

File "C:\Python27\lib\site-packages\sklearn\linear_model\base.py", line 200, in predict

return self._decision_function(X)

File "C:\Python27\lib\site-packages\sklearn\linear_model\base.py", line 183, in _decision_function

X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])

File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 407, in check_array

context))

ValueError: Found array with 0 sample(s) (shape=(0, 262)) while a minimum of 1 is required.