#!/usr/bin/env python
# coding: utf-8

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import numpy as np 
import pandas as pd 


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get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits 
img=load_digits()





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print(img["data"].shape) 
img_digits=img["data"] 
target=img["target"] 
plt.imshow(img_digits[0].reshape((8,8)),cmap="gray",) 
plt.title(label=target[0])


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df_img=pd.DataFrame() 
for i in range(64): 
    df_img[f"Pixel{i}"]=img_digits[:,i] 
    


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df_img


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from sklearn.preprocessing import MinMaxScaler 
scaler=MinMaxScaler() 
df_img_scaled=scaler.fit_transform(df_img)


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from sklearn.cluster import KMeans 
elbow={"inertia":[],"clusters":[]}

for i in range(2,15): 
    model=KMeans(i) 
    elbow["clusters"].append(i) 
    model.fit(df_img_scaled) 
    elbow["inertia"].append(model.inertia_)


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plt.scatter(x=elbow["clusters"],y=elbow["inertia"]) 
plt.xlabel("n_clusters") 
plt.ylabel("inertia") 
plt.title("Elbow Graph")


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model_10=KMeans(10) 
model_10.fit(df_img_scaled)


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from sklearn.manifold import TSNE 

tsne=TSNE(2) 
arr=tsne.fit_transform(df_img_scaled)


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plt.scatter(arr[:,0],arr[:,1],c=model_10.labels_)


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import plotly.offline as py 
import plotly.graph_objs as go 
import cufflinks as cf 
cf.offline 
py.init_notebook_mode()


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traces=[] 
for i in range(10):  
    mask=model_10.labels_==i
    traces.append(go.Scatter(x=arr[mask][:,0],y=arr[mask][:,1],name=f"cluster {i}",mode="markers")) 
data=go.Data(traces) 
layout=go.Layout(title="KMeans clustering for the digits") 
figure=go.Figure(data,layout) 
py.iplot(figure) 


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