Comments (5)
Hi @jumpingfella, the fit(...)
and transform(...)
functions from minirocket.py
require the data to be in the form of a 2d numpy array, with dtype np.float32
. Each row is a different time series, so if you have 10 timeseries, and each time series is of length 100, then your array should have 10 rows and 100 columns.
Does this answer your question?
from minirocket.
thanks, I had some progress with the following:
df = df.to_numpy()
df = df.astype(np.float32)
print(df.shape)
print(df)
which produces
(301, 2)
[[1.6197731e+12 5.4559469e+04]
[1.6197731e+12 5.4563930e+04]
[1.6197731e+12 5.4554230e+04]
[1.6197731e+12 5.4564340e+04]
but then I get
parameters = fit(df)
File "minirocket.py", line 130, in fit
biases = _fit_biases(X, dilations, num_features_per_dilation, quantiles)
ValueError: unable to broadcast argument 1 to output array
File "minirocket.py", line 77
from minirocket.
Hi @jumpingfella, if I understand correctly, you are treating the input as being 301 time series, each of length 2. I think it is more likely that one of your columns is a time series (i.e., you have 1 or 2 time series, each of length 301).
Could you describe your data in a little more detail, and what you are trying to do? Then I might be able to help a bit better. At the moment, I'm not sure whether MiniRocket is the right fit for your data.
Thanks very much.
from minirocket.
I'm trying to do classification of bitcoin price. In my data first column is a timestamp, second - close price. Data arrives in 5 seconds intervals. I'm guessing that I need to keep only close
column and do df = np.transpose(df)
from minirocket.
My problems are resolved with:
df = df.to_numpy()
df = df.astype(np.float32)
df = np.transpose(df)
Could be still nice to have full script which reads from CSV file.
from minirocket.
Related Issues (20)
- Feature Size HOT 2
- Unlabeled data HOT 2
- Can't set random_state when doing a gridsearchCV HOT 3
- Are minirocket.py and minirocket_dv.py the implementation for the same Mini-Rocket algorithm? HOT 1
- is is necessary to normalize data when use minirocket multivariable version? HOT 1
- datatype HOT 13
- some question about multivarible version. HOT 3
- if the kernel numbers is much less than dataset sample numbers
- is the code handle the padding right?
- Port to R language
- Datatype Error HOT 2
- Need example to use variable ROCKET HOT 1
- Feature Transformation HOT 13
- pls help to understand how your this repo code related to sktime.transformers.series_as_features.rocket HOT 1
- Can't reproduce the accuracy of 'EOGHorizontalSignal' dataset in UCR109 based on original 'MINIROCKET' exmples HOT 7
- Accuracy problem HOT 3
- padding problem
- how to use minirocket in production HOT 1
- Channels get lost HOT 4
- Any help for minirocket on UEA multivariate time series classification HOT 1
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from minirocket.