In [1]:

    

from sklearn import datasets
digits = datasets.load_digits(n_class=7)
X = digits.data
y = digits.target
from sklearn.preprocessing import normalize,scale
X = normalize(X)


    

In [3]:

    

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from IPython.html import widgets
from IPython.display import display

def plot_fixed(knn=None, knn_density=None, k_threshold=None):
    size=9
    plt.figure(figsize=(size, size))
    from eden.util.display import KernelQuickShiftTreeEmbedding, plot_embedding
    X_ = KernelQuickShiftTreeEmbedding(X, knn=knn, knn_density=knn_density, k_threshold=k_threshold)
    plot_embedding(X_, y)
    
    
slider_A = widgets.IntSliderWidget (min=3, max=24, step=1)
slider_B = widgets.IntSliderWidget (min=3, max=64, step=1)
slider_C = widgets.FloatSliderWidget (min=0.5, max=0.99, step=0.01)

w=widgets.interactive(plot_fixed, knn=slider_A, knn_density=slider_B, k_threshold=slider_C)
display(w)


    

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