Untitled.md

from sklearn.datasets import make_moons

X,y=make_moons(n_samples=200,noise=0.05,random_state=0)

import matplotlib.pyplot as plt
%matplotlib inline

plt.scatter(X[:,0],X[:,1])

<matplotlib.collections.PathCollection at 0x1a1b39e17c8>

from sklearn.cluster import KMeans,DBSCAN,AgglomerativeClustering

f,(ax1,ax2)=plt.subplots(1,2,figsize=(8,3))

km=KMeans(n_clusters=2,random_state=0)

y_km=km.fit_predict(X)

ax1.scatter(X[y_km==0,0],X[y_km==0,1],c='lightblue',marker='o',s=40,label='cluster 1')
ax1.scatter(X[y_km==1,0],X[y_km==1,1],c='red',marker='s',s=40,label='cluster 2')
ax1.set_title('K-means clustering')

Text(0.5, 1, 'K-means clustering')

ac=AgglomerativeClustering(n_clusters=2,affinity='euclidean',linkage='complete')
y_ac=ac.fit_predict(X)
ax2.scatter(X[y_ac==0,0],X[y_ac==0,1],c='lightblue',marker='o',s=40,label='cluster 1')
ax2.scatter(X[y_ac==1,0],X[y_ac==1,1],c='red',marker='s',s=40,label='cluster 2')
ax2.set_title('Agglomerative clustering')

Text(0.5, 1, 'Agglomerative clustering')

#f.legend()
f

db=DBSCAN(eps=0.2,min_samples=5,metric='euclidean')
y_db=db.fit_predict(X)
plt.scatter(X[y_db==0,0],X[y_db==0,1],c='lightblue',marker='o',s=40,label='cluster 1')
plt.scatter(X[y_db==1,0],X[y_db==1,1],c='red',marker='s',s=40,label='cluster 1')
plt.legend()
plt.show()

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

<ipython-input-22-2f755f117ac9> in <module>
----> 1 x[0]


NameError: name 'x' is not defined

km.cluster_centers_

array([[-0.2003285 ,  0.58035606],
       [ 1.20736718, -0.0825517 ]])

import shapely as sh

poly=sh.geometry.linestring.LineString([[0,0],[1,1]])

poly.distance(sh.geometry.point.Point([1,1]))

0.0

point=sh.geometry.point.Point([1,1])

point.distance(sh.geometry.point.Point([0,0]))

1.4142135623730951

import numpy as np

from itertools import permutations

list(permutations([1,2,3],2))

[(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)]

import gudhi

test=[i for i in range(100)]

te=gudhi.point_cloud.TimeDelayEmbedding(dim=5,delay=3,skip=1)

te(test)[19,4]

31

te(test)

array([[ 0,  3,  6,  9, 12],
       [ 1,  4,  7, 10, 13],
       [ 2,  5,  8, 11, 14],
       [ 3,  6,  9, 12, 15],
       [ 4,  7, 10, 13, 16],
       [ 5,  8, 11, 14, 17],
       [ 6,  9, 12, 15, 18],
       [ 7, 10, 13, 16, 19],
       [ 8, 11, 14, 17, 20],
       [ 9, 12, 15, 18, 21],
       [10, 13, 16, 19, 22],
       [11, 14, 17, 20, 23],
       [12, 15, 18, 21, 24],
       [13, 16, 19, 22, 25],
       [14, 17, 20, 23, 26],
       [15, 18, 21, 24, 27],
       [16, 19, 22, 25, 28],
       [17, 20, 23, 26, 29],
       [18, 21, 24, 27, 30],
       [19, 22, 25, 28, 31],
       [20, 23, 26, 29, 32],
       [21, 24, 27, 30, 33],
       [22, 25, 28, 31, 34],
       [23, 26, 29, 32, 35],
       [24, 27, 30, 33, 36],
       [25, 28, 31, 34, 37],
       [26, 29, 32, 35, 38],
       [27, 30, 33, 36, 39],
       [28, 31, 34, 37, 40],
       [29, 32, 35, 38, 41],
       [30, 33, 36, 39, 42],
       [31, 34, 37, 40, 43],
       [32, 35, 38, 41, 44],
       [33, 36, 39, 42, 45],
       [34, 37, 40, 43, 46],
       [35, 38, 41, 44, 47],
       [36, 39, 42, 45, 48],
       [37, 40, 43, 46, 49],
       [38, 41, 44, 47, 50],
       [39, 42, 45, 48, 51],
       [40, 43, 46, 49, 52],
       [41, 44, 47, 50, 53],
       [42, 45, 48, 51, 54],
       [43, 46, 49, 52, 55],
       [44, 47, 50, 53, 56],
       [45, 48, 51, 54, 57],
       [46, 49, 52, 55, 58],
       [47, 50, 53, 56, 59],
       [48, 51, 54, 57, 60],
       [49, 52, 55, 58, 61],
       [50, 53, 56, 59, 62],
       [51, 54, 57, 60, 63],
       [52, 55, 58, 61, 64],
       [53, 56, 59, 62, 65],
       [54, 57, 60, 63, 66],
       [55, 58, 61, 64, 67],
       [56, 59, 62, 65, 68],
       [57, 60, 63, 66, 69],
       [58, 61, 64, 67, 70],
       [59, 62, 65, 68, 71],
       [60, 63, 66, 69, 72],
       [61, 64, 67, 70, 73],
       [62, 65, 68, 71, 74],
       [63, 66, 69, 72, 75],
       [64, 67, 70, 73, 76],
       [65, 68, 71, 74, 77],
       [66, 69, 72, 75, 78],
       [67, 70, 73, 76, 79],
       [68, 71, 74, 77, 80],
       [69, 72, 75, 78, 81],
       [70, 73, 76, 79, 82],
       [71, 74, 77, 80, 83],
       [72, 75, 78, 81, 84],
       [73, 76, 79, 82, 85],
       [74, 77, 80, 83, 86],
       [75, 78, 81, 84, 87],
       [76, 79, 82, 85, 88],
       [77, 80, 83, 86, 89],
       [78, 81, 84, 87, 90],
       [79, 82, 85, 88, 91],
       [80, 83, 86, 89, 92],
       [81, 84, 87, 90, 93],
       [82, 85, 88, 91, 94],
       [83, 86, 89, 92, 95],
       [84, 87, 90, 93, 96],
       [85, 88, 91, 94, 97],
       [86, 89, 92, 95, 98],
       [87, 90, 93, 96, 99]])