Runqi Yang hitvoice

## compare.ipynb

      
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              January 9, 2023 08:49
            
              
                Compare sample efficiency
              
          
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## ReadMe.md

      
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              November 17, 2017 18:53
            
              
                Implementation of BNS scaling
              
          
    Implementation of BNS Scaling

Limitations:

for binary classification/OVR/OVO only
suitable for documents that are not too long

Advantage:

take class label into consideration, correct the inappropriate scaling by IDF
better than TF-IDF in most benchmarks

Pratical advice:

  
## plot_confusion_matrix.png

      
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                hitvoice
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              February 21, 2024 19:51
            
              
                 Generate matrix plot for confusion matrix with pretty annotations.
              
          
## histogram_animation.py
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.animation as manimation

FFMpegWriter = manimation.writers['ffmpeg']
writer = FFMpegWriter(fps=5)

fig = plt.figure(figsize=(12, 8))

## histogram_recover.py
import numpy as np
import matplotlib.pyplot as plt
# here's the precomputed histogram via `plt.hist` or `np.histogram`
bins = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]).astype(float)
counts = np.array([5, 3, 4, 5, 6, 1, 3, 7]).astype(float)
assert len(bins) == len(counts) + 1

# recover
centroids = (bins[1:] + bins[:-1]) / 2
counts_, bins_, _ = plt.hist(centroids, bins=len(counts),
	import numpy as np
	import matplotlib
	matplotlib.use("Agg")
	import matplotlib.pyplot as plt
	import matplotlib.animation as manimation

	FFMpegWriter = manimation.writers['ffmpeg']
	writer = FFMpegWriter(fps=5)

	fig = plt.figure(figsize=(12, 8))
	import numpy as np
	import matplotlib.pyplot as plt
	# here's the precomputed histogram via `plt.hist` or `np.histogram`
	bins = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]).astype(float)
	counts = np.array([5, 3, 4, 5, 6, 1, 3, 7]).astype(float)
	assert len(bins) == len(counts) + 1

	# recover
	centroids = (bins[1:] + bins[:-1]) / 2
	counts_, bins_, _ = plt.hist(centroids, bins=len(counts),