I am testing this code.
# Import the necessary packages
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Normalizer
from sklearn.cluster import KMeans
# Define a normalizer
normalizer = Normalizer()
# Create Kmeans model
kmeans = KMeans(n_clusters = 10,max_iter = 1000)
# Make a pipeline chaining normalizer and kmeans
pipeline = make_pipeline(normalizer,kmeans)
# Fit pipeline to daily stock movements
pipeline.fit(score)
labels = pipeline.predict(score)
This line throws an error:
pipeline.fit(score)
Here is the error that I see:
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
I don't know what this error means. I Googled it and didn't find anything useful. Here is a small sample of my data:
array=[1. 1. 1. ... 8. 1. 1.].
I am following the example from the link below.
When I run the code from the link, everything works fine. I'm not sure why it falls down when I run the code on my own data, which is just:
1, 1.9, 2.62, 3.5, 4.1, 7.7, 9.75, etc, etc.
It goes from 1-10. That's all it is.
Any sklearn.Transformer expects a [sample size, n_features] sized array. So there's two scenarios you will have to reshape your data,
So you need to do what suits the problem. You are passing a 1D vector.
[1. 1. 1. ... 8. 1. 1.]
If this is a single sample, reshape it to (1, -1) sized array and you will be fine. But with that said you might want to think about the following.