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kafkacat \
  -C \
  -b localhost:9092 \
  -t testTopic \
  -o beginning

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kafkacat \
  -C \
  -b localhost:9092 \
  -t testTopic \
  -o beginning \
  -c 10

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kafkacat \
  -C \
  -b localhost:9092 \
  -t testTopic \
  -c 10

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bin/kafka-simple-consumer-shell.sh \
  --bootstrap-server localhost:9092 \
  --topic testTopic \
  --partition 1 \
  --offset 12873

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kafkacat \
  -C \
  -b localhost:9092 \
  -t testTopic \
  -o -10 \
  -p 1 \
  -e

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kafkacat \
  -C \
  -b localhost:9092 \
  -t testTopic \
  -o 11 \
  -c 10 \
  -e

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"""
scikit-learn example to fit a SVC model for recognizing images of hand-written digits.

The images attribute of the dataset stores 8x8 arrays of grayscale values for each image. 
We will use these arrays to visualize the first 4 images. To apply a classifier on this data, 
we need to flatten the images, turning each 2-D array of grayscale values from 
shape (8, 8) into shape (64,). 

Reference: https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html
"""

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print(f'Classes: {clf.classes_}')
print(f'Class Weight: {clf.class_weight_}')
print(f'Intercept: {clf.intercept_}')

# Classes: [0 1 2 3 4 5 6 7 8 9]
# Class Weight: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# Intercept: [-0.41399582 -0.34935705 -0.26415146 -0.34374548 -0.43664007 -0.15259734
#  -0.29982087 -0.33727371 -0.31727775  0.16939161  0.23074142  0.12799678
#  0.09363933  0.30598209  0.13243965  0.33420154  0.20281266  0.09384162
#  0.01898659 -0.0295118   0.22687243  0.00904582 -0.02964941  0.02867172

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# Predict the digits in the test data
predictions = clf.predict(X_test)
print(predictions)

# [2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 9 2 0 1 2 6
#  3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6
#  8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2
#  3 4 5 6 7 1 9 0 1 2 3 4 5 6 9 0 1 2 3 4 5 6 7 8 9 4 9 5 5 6 5 0 9 8 9 8 4
#  1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 8 3 7 7 3 4 6 6 6 9 9 1 5 0 9 5 2 8 0 1
#  7 6 3 2 1 7 8 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 2 5 7 3

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sklearn.exceptions.NotFittedError: This SVC instance is not fitted yet. 
Call 'fit' with appropriate arguments before using this estimator.