Hi,
On Sep 8, 2009, at 9:09 AM, Abbas R. Ali wrote:
Hi Steve
I am facing a little problem in predict function which is I think
mismatch of dimension. Infacted area is covered by ***.
svm = function()
{
library(RODBC) # load RODBC library for database access
channel = odbcConnect("demo_dsn", "sa", "1234") # connecting to
the database with the dabtabase
data = sqlQuery(channel, "SELECT top 100 * FROM [Demographics].
[dbo].[CHA_Training]")
odbcClose(channel) # close the database connection
index = 1:nrow(data) # getting a vector of same size as data
sample_index <- sample(index, length(index) / 3) # samples of the
above vector
training <- data[-sample_index, ] # 2/3 training data
validation <- data[sample_index, ] # 1/3 test data
x = training[, length(training)]
# seperating class labels
model.ksvm = ksvm(x, data = training, kernel = "rbfdot", kpar= list
(sigma = 0.05), C = 5, cross = 3) # train data through SVM
*******************************************************************
Problamisitc area:
prSV = predict(model.ksvm, validation[, -length(validation)], type
= "decision") # validate data
You need to pass in data of the same dimension (# of cols) that you
trained on to your predict function.
You have already split your data into training and testing
(`training`, `validation`). Why are you removing certain dimensions
(features/columns) from your validation set when you pass it into the
predict function? ie:
predict(model.ksvm, validation[, -length(validation)]
should probably be
predict(model.ksvm, validation, ...)
That should work ... but if you're using this for anything serious, be
sure you understand why.
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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