blocks|key|4487110|text|这是我的函数(基于this)，用于清除数据集上的nan、Inf和丢失的单元格(对于倾斜的数据集)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4487111|import+pandas+as+pd

def+clean_dataset(df):
++++assert+isinstance(df,+pd.DataFrame),+"df+needs+to+be+a+pd.DataFrame"
++++df.dropna(inplace=True)
++++indices_to_keep+=+~df.isin([np.nan,+np.inf,+-np.inf]).any(1)
++++return+df[indices_to_keep].astype(np.float64)|code-block|syntax|javascript|4487112|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/152580/whats-the-canonical-way-to-check-for-type-in-python^0|O|3|S|3|9|4|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|H|7|10|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|11|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

This is my function (based on <a href="https://stackoverflow.com/questions/152580/whats-the-canonical-way-to-check-for-type-in-python">this</a>) to clean the dataset of <code>nan</code>, <code>Inf</code>, and missing cells (for skewed datasets):

<pre><code>import pandas as pd

def clean_dataset(df):
 assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
 df.dropna(inplace=True)
 indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
 return df[indices_to_keep].astype(np.float64)
</code></pre>

blocks|key|4486012|text|我的输入数组的维度是不对称的，因为我的输入csv有空格。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486013|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

The Dimensions of my input array were skewed, as my input csv had empty spaces.

blocks|key|4487299|text|在大多数情况下，摆脱无限和空值可以解决这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4487300|去掉无穷值。|4487301|df.replace([np.inf,+-np.inf],+np.nan,+inplace=True)|code-block|syntax|javascript|4487302|按您喜欢的方式删除空值、特定值，如999、mean，或者创建自己的函数来估算缺少的值|4487303|df.fillna(999,+inplace=True)|4487304|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Q|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

In most cases getting rid of infinite and null values solve this problem. 

get rid of infinite values.

<pre><code>df.replace([np.inf, -np.inf], np.nan, inplace=True)
</code></pre>

get rid of null values the way you like, specific value such as 999, mean, or create your own function to impute missing values 

<pre><code>df.fillna(999, inplace=True)
</code></pre>

blocks|key|4487072|text|使用此版本的python+3：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4487073|/opt/anaconda3/bin/python+--version
Python+3.6.0+::+Anaconda+4.3.0+(64-bit)|code-block|syntax|javascript|4487074|查看错误的详细信息，我发现了导致失败的代码行：|4487075|/opt/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py+in+_assert_all_finite(X)
+++++56+++++++++++++and+not+np.isfinite(X).all()):
+++++57+++++++++raise+ValueError("Input+contains+NaN,+infinity"
--->+58++++++++++++++++++++++++++"+or+a+value+too+large+for+%25r."+%25+X.dtype)
+++++59+
+++++60+

ValueError:+Input+contains+NaN,+infinity+or+a+value+too+large+for+dtype('float64').|4487076|从这里，我能够提取正确的方法来测试我的数据发生了什么，使用错误消息给出的失败的相同测试：np.isfinite(X)|offset|length|style|CODE|4487077|然后，通过快速而肮脏的循环，我能够发现我的数据确实包含nans|4487078|print(p[:,0].shape)
index+=+0
for+i+in+p[:,0]:
++++if+not+np.isfinite(i):
++++++++print(index,+i)
++++index+%2B=1

(367340,)
4454+nan
6940+nan
10868+nan
12753+nan
14855+nan
15678+nan
24954+nan
30251+nan
31108+nan
51455+nan
59055+nan
...|4487079|现在我要做的就是删除这些索引处的值。|4487080|entityMap^0|0|0|0|0|18|E|0|R|4|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|10|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|12|8|@$M|13|N|14|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|6|7|15|8|@$M|16|N|17|O|P]]|9|@]|A|$]]|$1|S|3|T|5|D|7|18|8|@]|9|@]|A|$E|F]]|$1|U|3|V|5|6|7|19|8|@]|9|@]|A|$]]|$1|W|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]]|X|$]]

With this version of python 3:

<pre><code>/opt/anaconda3/bin/python --version
Python 3.6.0 :: Anaconda 4.3.0 (64-bit)
</code></pre>

Looking at the details of the error, I found the lines of codes causing the failure:

<pre><code>/opt/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in _assert_all_finite(X)
 56 and not np.isfinite(X).all()):
 57 raise ValueError("Input contains NaN, infinity"
---&gt; 58 " or a value too large for %r." % X.dtype)
 59 
 60 

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
</code></pre>

From this, I was able to extract the correct way to test what was going on with my data using the same test which fails given by the error message: <code>np.isfinite(X)</code>

Then with a quick and dirty loop, I was able to find that my data indeed contains <code>nans</code>:

<pre><code>print(p[:,0].shape)
index = 0
for i in p[:,0]:
 if not np.isfinite(i):
 print(index, i)
 index +=1

(367340,)
4454 nan
6940 nan
10868 nan
12753 nan
14855 nan
15678 nan
24954 nan
30251 nan
31108 nan
51455 nan
59055 nan
...
</code></pre>

Now all I have to do is remove the values at these indexes.

blocks|key|4486421|text|这里的答案对我都不起作用。这就是有效的方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486422|Test_y+=+np.nan_to_num(Test_y)|code-block|syntax|javascript|4486423|它将无穷大值替换为高有限值，将nan值替换为数字|4486424|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

None of the answers here worked for me. This was what worked.
<pre><code>Test_y = np.nan_to_num(Test_y)
</code></pre>
It replaces the infinity values with high finite values and the nan values with numbers

blocks|key|4487148|text|在尝试选择行子集后出现错误：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4487149|df+=+df.reindex(index=my_index)|code-block|syntax|javascript|4487150|原来my_index包含df.index中不包含的值，因此reindex函数插入了一些新行，并用nan填充它们。|offset|length|style|CODE|4487151|entityMap^0|0|0|2|8|C|8|1C|3|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]|$I|T|J|U|K|L]|$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|X|8|@]|9|@]|A|$]]]|N|$]]

I had the error after trying to select a subset of rows:

<pre><code>df = df.reindex(index=my_index)
</code></pre>

Turns out that <code>my_index</code> contained values that were not contained in <code>df.index</code>, so the reindex function inserted some new rows and filled them with <code>nan</code>.

blocks|key|4487032|text|我也犯了同样的错误，在我的例子中，x和y是数据帧，所以我必须先把它们转换成矩阵：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4487033|X+=+X.values.astype(np.float)
y+=+y.values.astype(np.float)|code-block|syntax|javascript|4487034|编辑:最初建议的X.as_matrix()为Deprecated|offset|length|4487035|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/version/0.25.1/reference/api/pandas.DataFrame.as_matrix.html^0|0|0|M|A|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@$I|V|J|W|1|X]]|A|$]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|R]]]]

I had the same error, and in my case X and y were dataframes so I had to convert them to matrices first:
<pre><code>X = X.values.astype(np.float)
y = y.values.astype(np.float)
</code></pre>
Edit: The originally suggested X.as_matrix() is <a href="https://pandas.pydata.org/pandas-docs/version/0.25.1/reference/api/pandas.DataFrame.as_matrix.html" rel="nofollow noreferrer">Deprecated</a>

blocks|key|4486371|text|删除所有无限值：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486372|(并替换为该列的最小值或最大值)|4486373|import+numpy+as+np

#+generate+example+matrix
matrix+=+np.random.rand(5,5)
matrix[0,:]+=+np.inf
matrix[2,:]+=+-np.inf
>>>+matrix
array([[+++++++inf,++++++++inf,++++++++inf,++++++++inf,++++++++inf],
+++++++[0.87362809,+0.28321499,+0.7427659+,+0.37570528,+0.35783064],
+++++++[++++++-inf,+++++++-inf,+++++++-inf,+++++++-inf,+++++++-inf],
+++++++[0.72877665,+0.06580068,+0.95222639,+0.00833664,+0.68779902],
+++++++[0.90272002,+0.37357483,+0.92952479,+0.072105++,+0.20837798]])

#+find+min+and+max+values+for+each+column,+ignoring+nan,+-inf,+and+inf
mins+=+[np.nanmin(matrix[:,+i][matrix[:,+i]+!=+-np.inf])+for+i+in+range(matrix.shape[1])]
maxs+=+[np.nanmax(matrix[:,+i][matrix[:,+i]+!=+np.inf])+for+i+in+range(matrix.shape[1])]

#+go+through+matrix+one+column+at+a+time+and+replace++%2B+and+-infinity+
#+with+the+max+or+min+for+that+column
for+i+in+range(matrix.shape[1]):
++++matrix[:,+i][matrix[:,+i]+==+-np.inf]+=+mins[i]
++++matrix[:,+i][matrix[:,+i]+==+np.inf]+=+maxs[i]

>>>+matrix
array([[0.90272002,+0.37357483,+0.95222639,+0.37570528,+0.68779902],
+++++++[0.87362809,+0.28321499,+0.7427659+,+0.37570528,+0.35783064],
+++++++[0.72877665,+0.06580068,+0.7427659+,+0.00833664,+0.20837798],
+++++++[0.72877665,+0.06580068,+0.95222639,+0.00833664,+0.68779902],
+++++++[0.90272002,+0.37357483,+0.92952479,+0.072105++,+0.20837798]])|code-block|syntax|javascript|4486374|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

<h1>Remove all infinite values:</h1>
<h3>(and replace with min or max for that column)</h3>
<pre><code>import numpy as np

# generate example matrix
matrix = np.random.rand(5,5)
matrix[0,:] = np.inf
matrix[2,:] = -np.inf
&gt;&gt;&gt; matrix
array([[ inf, inf, inf, inf, inf],
 [0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
 [ -inf, -inf, -inf, -inf, -inf],
 [0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
 [0.90272002, 0.37357483, 0.92952479, 0.072105 , 0.20837798]])

# find min and max values for each column, ignoring nan, -inf, and inf
mins = [np.nanmin(matrix[:, i][matrix[:, i] != -np.inf]) for i in range(matrix.shape[1])]
maxs = [np.nanmax(matrix[:, i][matrix[:, i] != np.inf]) for i in range(matrix.shape[1])]

# go through matrix one column at a time and replace + and -infinity 
# with the max or min for that column
for i in range(matrix.shape[1]):
 matrix[:, i][matrix[:, i] == -np.inf] = mins[i]
 matrix[:, i][matrix[:, i] == np.inf] = maxs[i]

&gt;&gt;&gt; matrix
array([[0.90272002, 0.37357483, 0.95222639, 0.37570528, 0.68779902],
 [0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
 [0.72877665, 0.06580068, 0.7427659 , 0.00833664, 0.20837798],
 [0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
 [0.90272002, 0.37357483, 0.92952479, 0.072105 , 0.20837798]])
</code></pre>

blocks|key|4486224|text|我得到了同样的错误。在进行任何替换、替换等操作之前，它与df.fillna(-99999,+inplace=True)一起工作|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4486225|entityMap^0|S|V|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

i got the same error. it worked with <code>df.fillna(-99999, inplace=True)</code> before doing any replacement, substitution etc

blocks|key|4487478|text|DecisionTreeClassifier输入检查出现问题，请尝试|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4487479|X_train+=+X_train.replace((np.inf,+-np.inf,+np.nan),+0).reset_index(drop=True)|code-block|syntax|javascript|4487480|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Problem seems to occur in DecisionTreeClassifier input check, Try
<pre><code>X_train = X_train.replace((np.inf, -np.inf, np.nan), 0).reset_index(drop=True)
</code></pre>

blocks|key|4486261|text|在我的例子中，问题是许多scikit函数返回numpy数组，这些数组没有pandas索引。因此，当我使用这些numpy数组构建新的DataFrames，然后尝试将它们与原始数据混合时，会出现索引不匹配的情况。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486262|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

In my case the problem was that many scikit functions return numpy arrays, which are devoid of pandas index. So there was an index mismatch when I used those numpy arrays to build new DataFrames and then I tried to mix them with the original data.

blocks|key|4486399|text|我想为numpy提出一个适合我的解决方案。这条线|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486400|from+numpy+import+inf
inputArray[inputArray+==+inf]+=+np.finfo(np.float64).max|code-block|syntax|javascript|4486401|用最大float64数替换numpy数组的所有infite值。|4486402|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

I would like to propose a solution for numpy that worked well for me. The line
<pre><code>from numpy import inf
inputArray[inputArray == inf] = np.finfo(np.float64).max
</code></pre>
substitues all infite values of a numpy array with the maximum float64 number.

blocks|key|4486439|text|dataset+=+dataset.dropna(axis=0,+how='any',+thresh=None,+subset=None,+inplace=False)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4486440|这对我很有效|unstyled|4486441|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|J|8|@]|9|@]|A|$]]|$1|G|3|-4|5|F|7|K|8|@]|9|@]|A|$]]]|H|$]]

<pre><code>dataset = dataset.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
</code></pre>
This worked for me

blocks|key|4487455|text|如果你在运行一个估计器，可能是你的学习率太高了。我也意外地传入了错误的数组网格搜索，最终以500的学习率进行训练，我认为这会导致训练过程中出现问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4487456|基本上，不一定只是你的输入必须全部有效，中间数据也是如此。|4487457|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

If you're running an estimator, it could be that your learning rate is too high. I passed in the wrong array too a grid search by accident and ended up training with a learning rate of 500, which I could see causing issues with the training process.
Basically it's not necessarily only your inputs that have to all be valid, but the intermediate data as well.

blocks|key|4486523|text|我也遇到了同样的问题，在我的例子中，答案很简单:我的CSV中有一个没有值的单元格("x，y，z，，")。在中设置一个默认值可以帮我解决这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486524|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I had the same issue, in my case the answer was simply that I had a cell in my CSV with no value (&quot;x,y,z,,&quot;). Putting a default value in fixed it for me.

blocks|key|4486545|text|使用isneginf可能会有所帮助。http://docs.scipy.org/doc/numpy/reference/generated/numpy.isneginf.html#numpy.isneginf|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4486546|x[numpy.isneginf(x)]+=+0+#0+is+the+value+you+want+to+replace+with|code-block|syntax|javascript|4486547|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/reference/generated/numpy.isneginf.html#numpy.isneginf^0|2|8|I|2E|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

Using <code>isneginf</code> may help.
<a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.isneginf.html#numpy.isneginf" rel="nofollow noreferrer">http://docs.scipy.org/doc/numpy/reference/generated/numpy.isneginf.html#numpy.isneginf</a>
<pre><code>x[numpy.isneginf(x)] = 0 #0 is the value you want to replace with
</code></pre>

blocks|key|4486570|text|在对新列调用pct_change之后，我发现nan存在于其中一行中。我使用以下代码删除了nan行|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486571|df+=+df.replace([np.inf,+-np.inf],+np.nan)
df+=+df.dropna()
df+=+df.reset_index()|code-block|syntax|javascript|4486572|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I found that after calling pct_change on a new column that nan existed in one of rows. I remove the nan row with the following code
<pre><code>df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.reset_index()
</code></pre>

blocks|key|4486297|text|试一试|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4486298|mat.sum()|code-block|syntax|javascript|4486299|如果你的数据总和是无穷大(大于最大浮点值3.402823e%2B38)，你就会得到这个错误。|4486300|请参阅scikit源代码中的validation.py中的_assert_all_finite函数：|4486301|if+is_float+and+np.isfinite(X.sum()):
++++pass
elif+is_float:
++++msg_err+=+"Input+contains+{}+or+a+value+too+large+for+{!r}."
++++if+(allow_nan+and+np.isinf(X).any()+or
++++++++++++not+allow_nan+and+not+np.isfinite(X).all()):
++++++++type_err+=+'infinity'+if+allow_nan+else+'NaN,+infinity'
++++++++#+print(X.sum())
++++++++raise+ValueError(msg_err.format(type_err,+X.dtype))|4486302|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|S|8|@]|9|@]|A|$E|F]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

try 

<pre><code>mat.sum()
</code></pre>

If the sum of your data is infinity (greater that the max float value which is 3.402823e+38) you will get that error.

see the _assert_all_finite function in validation.py from the scikit source code:

<pre><code>if is_float and np.isfinite(X.sum()):
 pass
elif is_float:
 msg_err = "Input contains {} or a value too large for {!r}."
 if (allow_nan and np.isinf(X).any() or
 not allow_nan and not np.isfinite(X).all()):
 type_err = 'infinity' if allow_nan else 'NaN, infinity'
 # print(X.sum())
 raise ValueError(msg_err.format(type_err, X.dtype))
</code></pre>

I am using sklearn and having a problem with the affinity propagation. I have built an input matrix and I keep getting the following error. 

<pre><code>ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
</code></pre>

I have run

<pre><code>np.isnan(mat.any()) #and gets False
np.isfinite(mat.all()) #and gets True
</code></pre>

I tried using

<pre><code>mat[np.isfinite(mat) == True] = 0
</code></pre>

to remove the infinite values but this did not work either. 
What can I do to get rid of the infinite values in my matrix, so that I can use the affinity propagation algorithm?

I am using anaconda and python 2.7.9.

sklearn error ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在使用sklearn，但在亲和力传播方面遇到了问题。我已经构建了一个输入矩阵，并且一直收到以下错误。ValueError: Input contains NaN, infinity or a value too large for dtype('float64').我跑过了np.isnan(mat.any()) #and gets Falsenp.isfinite(mat.all()) #an

问sklearn错误ValueError:输入包含NaN、无穷大或对于dtype('float64')来说太大的值
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