Hi.
The example is meant to illustrate the prediction boundaries on a 2d
example.
You can not plot the decision boundaries like that for higher dimensions.
Just predicting should work fine, though.
Hth,
Andy
On 11/19/2014 11:56 AM, John Doe wrote:
I have just started using scikit and I have been following the example
code at
http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
I am having some trouble with my data, here is the code that I have
been using to import my data from csv files that I dump out of a database.
query_csv_file = 'query_result.csv'
X = np.loadtxt(fname = query_csv_file, delimiter = ',', skiprows = 1)
id_csv_file = 'query_id.csv'
y = np.loadtxt(fname = id_csv_file, delimiter = ‘,', skiprows = 1)
query_result.csv looks like the following:
attr1,attr2,attr3,attr4,attr5,attr6,attr7,attr8,attr9,attr10,attr11,attr12,attr13,attr14,attr15,attr16,attr17,attr18
0,797.3700774,0,330.8733288,155.4552568,0,6177.289831,0,355.3611942,82.92122107,0,49.93354722,1592.292597,435.3831843,721.4373622,73.07706747,194.5707382,0
query_id.csv looks like the following:
id
1
If I try to run the code in the example using this data I get the
following error:
ValueError: Incompatible dimension for X and Y matrices: X.shape[1] ==
2 while Y.shape[1] == 18
If I add the following line of code it works but then it is not using
all 18 attributes:
X = X[:, :2]
Any help or guidance would be appreciated! Thanks in advance!
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