এসসিপি স্প্যাশিয়াল

from scipy import spatial

data = [
    [1, 2],
    [3, 4],
    [8, 9],
    [10, 12]
]

tree = spatial.KDTree(data)

result = tree.query([4, 5])

print(result)

from scipy import spatial

data = [
    [1, 2],
    [3, 4],
    [8, 9],
    [10, 12]
]

tree = spatial.cKDTree(data)

result = tree.query([4, 5])

print(result)

from scipy import spatial

first = [1, 2]
second = [4, 6]

result = spatial.distance.minkowski(
    first,
    second,
    p=2
)

print(result)

from scipy import spatial

first = [1, 2]
second = [4, 6]

result = spatial.distance.euclidean(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 2]
second = [4, 6]

result = spatial.distance.cityblock(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 2, 3]
second = [2, 4, 6]

result = spatial.distance.cosine(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 2, 3, 4]
second = [2, 4, 6, 8]

result = spatial.distance.correlation(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 0, 1, 1]
second = [1, 1, 0, 1]

result = spatial.distance.jaccard(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 0, 1, 1]
second = [1, 1, 0, 1]

result = spatial.distance.hamming(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 2]
second = [4, 6]

result = spatial.distance.chebyshev(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 2, 3]
second = [2, 4, 6]

result = spatial.distance.canberra(
    first,
    second
)

print(result)

from scipy import spatial

first = [1, 2, 3]
second = [2, 4, 5]

result = spatial.distance.braycurtis(
    first,
    second
)

print(result)

from scipy import spatial

data = [
    [1, 2],
    [3, 4],
    [5, 6]
]

result = spatial.distance.pdist(
    data
)

print(result)

from scipy import spatial

first = [
    [1, 2],
    [3, 4]
]

second = [
    [5, 6],
    [7, 8]
]

result = spatial.distance.cdist(
    first,
    second
)

print(result)

from scipy import spatial

data = [
    [1, 2],
    [3, 4],
    [5, 6]
]

distances = spatial.distance.pdist(data)

result = spatial.distance.squareform(
    distances
)

print(result)

from scipy import spatial

data = [
    [0, 0],
    [2, 0],
    [2, 2],
    [0, 2],
    [1, 1]
]

result = spatial.ConvexHull(data)

print(result.vertices)

from scipy import spatial

data = [
    [0, 0],
    [2, 0],
    [0, 2],
    [2, 2]
]

result = spatial.Delaunay(data)

print(result.simplices)

from scipy import spatial

data = [
    [0, 0],
    [2, 0],
    [1, 2]
]

result = spatial.Voronoi(data)

print(result.vertices)

from scipy import spatial

data = [
    [1, 2],
    [3, 4],
    [5, 6]
]

result = spatial.distance.pdist(data)

print(result)