from scipy.spatial import distance
import numpy as np

import matplotlib.pyplot as plt
%matplotlib inline

import matplotlib.pyplot as plt
%matplotlib inline

plt.plot(0,0,marker='o',c='blue')
plt.plot(4,3,marker='o',c='red')
plt.plot([0,4],[0,3],linestyle='dashed',c='green')
plt.title('Euclid Distance')
plt.axis('square')
plt.xlim(-5,5)
plt.ylim(-5,5)
plt.show()

from scipy.spatial import distance

a = [0,0]
b = [4,3]

# ユークリッド距離を求める
euclid_distance = distance.euclidean(a, b)
print(euclid_distance)

from scipy.spatial import distance

a = [0,0,0]
b = [1,1,1]

# ユークリッド距離を求める
euclid_distance = distance.euclidean(a, b)
print(euclid_distance)

import matplotlib.pyplot as plt
%matplotlib inline

plt.plot(0,0,marker='o',c='blue')
plt.plot(4,3,marker='o',c='red')
plt.plot([0,4],[0,0],linestyle='dashed',c='green')
plt.plot([4,4],[0,3],linestyle='dashed',c='green')
plt.title('Manhattan Distance')
plt.axis('square')
plt.xlim(-5,5)
plt.ylim(-5,5)
plt.show()

import numpy as np

a = np.array([0, 0])
b = np.array([4, 3])

# マンハッタン距離を求める
manhattan_distance = np.linalg.norm(a - b , ord=1)
print(manhattan_distance)

import numpy as np

a = np.array([0, 0, 0])
b = np.array([4, 4, 4])

# マンハッタン距離を求める
manhattan_distance = np.linalg.norm(a - b , ord=1)
print(manhattan_distance)

import matplotlib.pyplot as plt
%matplotlib inline

plt.plot(0,0,marker='o',c='blue')
plt.plot(4,3,marker='o',c='red')
plt.plot([0,4],[0,0],linestyle='dashed',c='green')
plt.plot([4,4],[0,3],linestyle='dashed',c='green')
plt.title('Chebyshev Distance')
plt.axis('square')
plt.xlim(-5,5)
plt.ylim(-5,5)
plt.show()

from scipy.spatial import distance

a = [0 , 0]
b = [4 , 3]

# チェビシェフ距離を求める
chebyshev_distance = distance.chebyshev(a , b)
print(chebyshev_distance)

from scipy.spatial import distance

a = np.array([0, 0, 0])
b = np.array([1, 2, 4])

# チェビシェフ距離を求める
chebyshev_distance = distance.chebyshev(a , b)
print(chebyshev_distance)

from scipy.spatial import distance

a = [0 , 0]
b = [4 , 3]

# ミンコフスキー距離を求める p=1
minkowski_distance = distance.minkowski(a,b,p=1)
print(minkowski_distance)

# ミンコフスキー距離を求める p=2
minkowski_distance = distance.minkowski(a,b,p=2)
print(minkowski_distance)

# ミンコフスキー距離を求める p=99
minkowski_distance = distance.minkowski(a,b,p=99)
print(minkowski_distance)

!pip install python-Levenshtein

import Levenshtein

s1 = 'abcde'
s2 = 'abdfcee'

# レーベンシュタイン距離を算出する
levenshtein_distance = Levenshtein.distance(s1, s2)
print(levenshtein_distance)

import Levenshtein

s1 = 'abcde'
s2 = 'abdfc'

# ジャロ・ウィンクラー距離を算出する
jaro_winkler_distance = Levenshtein.jaro_winkler(s1, s2)
print(jaro_winkler_distance)

import matplotlib.pyplot as plt
from matplotlib import patches

# 2つのベクトルを指定
a = np.array([5.0, 5.0])
b = np.array([3.0, 9.0])

# 作図
fig, ax = plt.subplots()
plt.quiver(0, 0, a[0], a[1], angles='xy', scale_units='xy', scale=1, color='c', label='a')
plt.quiver(0, 0, b[0], b[1], angles='xy', scale_units='xy', scale=1, color='orange', label='b')
c = patches.Arc(xy=(0.9, 1.5), width=0.5, height=0.3, angle=-60, 
                theta1=0, theta2=180, linewidth=3, color="r")
ax.add_patch(c)
plt.title('Cosine Similarity')
plt.axis('square')
plt.xlim(-2,10)
plt.ylim(-2,10)
plt.legend()
plt.grid()
plt.show()

from scipy.spatial import distance

a = [2, 48, 9, 11]
b = [3, 51, 13, 19]

cosine_dos = 1 - distance.cosine(a , b)
print(cosine_dos)

from scipy.spatial import distance

a = [1 , 1]
b = [-1 , -1]

cosine_dos = 1 - distance.cosine(a , b)
print(cosine_dos)

from scipy.spatial import distance

a = [2,3,4,5]
b = [2,3,5,6]

correlation_coefficient =  1 - distance.correlation(a , b)
print(correlation_coefficient)

from scipy.spatial import distance

a = [2,3,4,5]
b = [5,4,3,1]

correlation_coefficient =  1 - distance.correlation(a , b)
print(correlation_coefficient)

import numpy as np

# ダイス係数を算出する
def dice_coefficient(x,y):
    num = len(set.intersection(set(x), set(y)))
    if num!=0:
        return (2.0 * num) / (len(x) + len(y))
    else:
        return 0.0
    
a = np.array([1, 2, 3, 5])
b = np.array([1, 2, 3, 4, 6])

dice_index = dice_coefficient(a,b)
print(dice_index)