The last line is giving me error.I am trying to solve the Titanic dataset problem of kraggle.
The error is in above line. You forgot to close the brackets there.
Change this:
print(clf.predict(x_test[1:10])
to
print(clf.predict(x_test[1:10]))
And even after that you will get error in the line:
clf.f1_score(...)
The above line is wrong, it should be:
f1_score(y_test, clf.predict(x_test))
Related
a=1
% Construct the trasfer function
num=[a 1 3]
den=[1 2 10]
G=tf(num,den)
% Impulse response
impulse(G)
% Step response
step(G)
When I click on 'run' this error appears "error: Order numerator >= order denominator"
If you follow the error on the terminal, it suggests that line 95 in imp_invar.m of the control package is to blame. (if you don't know where this was installed, you can find out by typing pkg list in your terminal)
If you convert this error to a warning, the code continues. Obviously you do so at your own risk. I would make a backup of the original .m file just in case.
Note that the same code run on matlab does not issue any error or warning (which is odd in itself, given the stark note about invalid impulse invariance in this scenario from octave ... there is a reference quoted inside imp_invar.m if you're interested.)
A program to run a Schmid-Leiman transformation using SPSS's Matrix language was published in 2005 by Woolf & Preising in Behavior Research Methods volume 37, pages 48 to 58). It is probably not important for you to know what a Schmid-Leiman transformation is, but I'll explain in comments if you feel it is necessary.
In modifying the program for my own data, I'm getting an error I can't figure out:
Error # 12302 in column 12. Text: ,
Syntax error.
Execution of this command stops.
Error in RIGHT HAND SIDE of COMPUTE command.
The MATRIX statement skipped.
Here is the beginning of the code. The error is showing as coming in Line 6:
* Encoding: UTF-8.
* Schmid-Leiman Solution for 2 level higher-order Factor analysis.
Matrix.
* ENTER YOUR SPECIFICATIONS HERE.
* Enter first-order pattern matrix.
Compute F1={.461, .253, -.058, -.069;
.241, .600, .143, .033;
.582, .047, -.077, -.125;
.327, .297, -.120, -.166;
.176, .448, -.240, -.099;
.680, .069, -.036, -.138;
.415, .228, -.091, -.153;
.
.
.
.390, .205, .002, -.098;
.164, .369, -.170, -.047
}.
As shown above, the text generating the error is shown as a comma (,), but the actual text (following the COMPUTE statement) in column 12 is an open bracket ({). So I have no idea what is going on. Can someone help?
For reference, the original code as proposed by Woolf & Preising (2005) is found here;
The Woolf & Preising article is found here
PS: The sample program given in the link above does run on my copy of SPSS. Here's the beginning of that code:
* Schmid-Leiman Solution for 2 level higher-order Factor analysis.
Matrix.
* ENTER YOUR SPECIFICATIONS HERE.
* Enter first-order pattern matrix.
Compute F1={0.099, 0.5647, -0.1521;
0.0124, 0.9419, -0.1535;
-0.1501, 0.6177, 0.4218;
0.7441, -0.0882, 0.1425;
0.6241, 0.2793, -0.1137;
0.8693, -0.0331, 0.0289;
-0.0154, -0.2706, 0.6262;
-0.0914, 0.0995, 0.7216;
0.1502, 0.0835, 0.398}.
Documentation is not helpful to me at all.
First, I tried using set() ,but I don't understand what it means by
set an instance for future calls
I could successfully feed my data using my dataset's structure described below.
So, I am not sure why I need to use set for that as it mentioned.
Here is my feature sequence of type scipy.sparse after I called nonzero() method.
[['66=1', '240=1', '286=1', '347=10', '348=1'],...]
where ... imply, same structure as previous elements
Second problem I encountered is Tagger.probability() and Tagger.marginal().
For Tagger.probability, I used the same input as Tagget.tag(), and I get this follwoing error.
and if my input is just a list instead of list of list. I get the following error.
Traceback (most recent call last):
File "cliner", line 60, in <module>
main()
File "cliner", line 49, in main
train.main()
File "C:\Users\Anak\PycharmProjects\CliNER\code\train.py", line 157, in main
train(training_list, args.model, args.format, args.use_lstm, logfile=args.log, val=val_list, test=test_list)
File "C:\Users\Anak\PycharmProjects\CliNER\code\train.py", line 189, in train
model.train(train_docs, val=val_docs, test=test_docs)
File "C:\Users\Anak\PycharmProjects\CliNER\code\model.py", line 200, in train
test_sents=test_sents, test_labels=test_labels)
File "C:\Users\Anak\PycharmProjects\CliNER\code\model.py", line 231, in train_fit
dev_split=dev_split )
File "C:\Users\Anak\PycharmProjects\CliNER\code\model.py", line 653, in generic_train
test_X=test_X, test_Y=test_Y)
File "C:\Users\Anak\PycharmProjects\CliNER\code\machine_learning\crf.py", line 220, in train
train_pred = predict(model, X) # ANAK
File "C:\Users\Anak\PycharmProjects\CliNER\code\machine_learning\crf.py", line 291, in predict
print(tagger.probability(xseq[0]))
File "pycrfsuite/_pycrfsuite.pyx", line 650, in pycrfsuite._pycrfsuite.Tagger.probability
ValueError: The numbers of items and labels differ: |x| = 12, |y| = 73
For Tagger.marginal(), I can only produce error similar to first error shown of Tagger.probabilit().
Any clue on how to use these 3 methods?? Please give me shorts example of use cases of these 3 methods.
I feel like there must be some example of these 3 methods, but I couldn't find one. Am I looking at the right place. This is the website I am reading documentation from
Additional info: I am using CliNER. in case any of you are familiar with it.
https://python-crfsuite.readthedocs.io/en/latest/pycrfsuite.html
I know this questions is over a year old, but I just had to figure out the same thing as well -- I am also leveraging some of the CliNER framework. For the CliNER specific solution, I forked the repo and rewrote the predict method in the ./code/machine_learning/crf.py file
To obtain the marginal probability, you need to add the following line to the for loop that iterates over the pycrf_instances after yseq is created (see line 196 here)
y_probs = [tagger.marginal(y, ii) for ii, y in enumerate(yseq)]
And then you can return that list of marginal probabilities from the predict method -- you will in turn be required to rewrite additional functions in the to accommodate this change.
Can anyone help me with conversion of the following code to vectorized form ??
z=X*theta;
g=sigmoid(z);
for i=1:m
J=J+((-y(i)*log(g(i)))-((1-y(i))*log(1-g(i))));
end
I have tried the following
z=X*theta;
g=sigmoid(z);
J=J+((-y).*log(g)-((1-y).*log(1-g)));
but this prints a particular value m times. and also wrong answer.
the answer for J is 0.693 which i get right using for loop but for the vectorized implementation i get 0.00693.
What's wrong?
Thanks in advance.
You are taking J, and adding all the -y(i)*log(g(i))-(1-y(i))*log(1-g(i)) to it.
So you need to take J and add (sum) all those things.
J=J+sum( (-y).*log(g)-(1-y).*log(1-g) );
min-one-of is reporting an error which I do not understand. I understand that min-one-of requires a reporter procedure. I have this, which is noted as reporter-procedure below.
However my reporter procedures takes as its input the output of another procedure. My intention is that the output of other-procedure will be fed into reporter-procedure as its input.
Netlogo is telling me that this other procedure, other-procedure in the code section below, is expected to be a reporter. Why?
Note that other-procedure takes four variables as inputs.
let best-taxi min-one-of available-taxis [reporter-procedure ( other-procedure var1 var2 var3 var4) ]
Now if I try this without parentheses, the error message is that reporter-procedure expected 1 input:
let best-taxi min-one-of available-taxis [reporter-procedure other-procedure var1 var2 var3 var4]
So I am guessing reporter-procedure executes first. If so, why doesn't the first set of code (the code with parentheses) work by making other-procedure execute first?
The error doesn't actually have anything to do with with min-one-of.
My intention is that the output of other-procedure will be fed into reporter-procedure as its input.
Only reporters have output. For other-procedure to have output that's fed into reporter-procedure, other-procedure must be a reporter.
You probably just need to declare other-procedure with to-report instead of to and make sure there's a report at the end of it with the output.