This is Ruby/Numo::NArray version of 100 numpy exercises (Repository)
require "numo/narray"
Python:
print(np.__version__)
np.show_config()
Ruby:
p Numo::NArray::VERSION
z = Numo::DFloat.zeros(10)
p z
Python:
Z = np.zeros((10,10))
print("%d bytes" % (Z.size * Z.itemsize))
Ruby:
z = Numo::DFloat.zeros(10,10)
printf "%d bytes", z.byte_size
Python:
$ python -c "import numpy; numpy.info(numpy.add)"
Ruby:
ri 'Numo::DFloat#+'
Python:
Z = np.zeros(10)
Z[4] = 1
print(Z)
Ruby:
z = Numo::DFloat.zeros(10)
z[4] = 1
p z
Python:
Z = np.arange(10,50)
print(Z)
Ruby:
z = Numo::DFloat[10..49]
p z
Python:
Z = np.arange(50)
Z = Z[::-1]
print(Z)
Ruby:
z = Numo::Int32.new(50).seq
z = z.reverse
Python:
Z = np.arange(9).reshape(3,3)
print(Z)
Ruby:
z = Numo::Int32.new(3,3).seq
p z
Python:
nz = np.nonzero([1,2,0,0,4,0])
print(nz)
Ruby:
nz = Numo::NArray[1,2,0,0,4,0].ne(0).where
p nz
z = Numo::DFloat.eye(3)
p z
Python:
Z = np.random.random((3,3,3))
print(Z)
Ruby:
z = Numo::DFloat.new(3,3,3).rand
p z
Python:
Z = np.random.random((10,10))
Zmin, Zmax = Z.min(), Z.max()
print(Zmin, Zmax)
Ruby:
z = Numo::DFloat.new(10,10).rand
zmin, zmax = z.minmax
p zmin, zmax
Python:
Z = np.random.random(30)
m = Z.mean()
print(m)
Ruby:
z = Numo::DFloat.new(30).rand
m = z.mean
p m
Python:
Z = np.ones((10,10))
Z[1:-1,1:-1] = 0
print(Z)
Ruby:
z = Numo::DFloat.ones(10,10)
z[1..-2,1..-2] = 0
p z
Python:
Z = np.ones((5,5))
Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)
print(Z)
Ruby:
# todo: pad
Python:
print(0 * np.nan)
print(np.nan == np.nan)
print(np.inf > np.nan)
print(np.nan - np.nan)
print(0.3 == 3 * 0.1)
Ruby:
0 * Float::NAN
Float::NAN == Float::NAN
Float::INFINITY > Float::NAN
Float::NAN - Float::NAN
0.3 == 3 * 0.1
Python:
Z = np.diag(1+np.arange(4),k=-1)
print(Z)
Ruby:
z = Numo::Int32.zeros(5,5)
z.diagonal(-1)[] = Numo::Int32[1..4]
p z
Python:
Z = np.zeros((8,8),dtype=int)
Z[1::2,::2] = 1
Z[::2,1::2] = 1
print(Z)
Ruby:
# todo: rangewithstep
x = Numo::Int32.new(1,8).seq
y = Numo::Int32.new(8,1).seq
z = (x+y)%2
p z
Python:
print(np.unravel_index(100,(6,7,8)))
Ruby:
# NArray allows unraveled index access
z = Numo::Int32.new(6,7,8).seq
p z[100]
Python:
Z = np.tile( np.array([[0,1],[1,0]]), (4,4))
print(Z)
Ruby:
# todo: tile
Python:
Z = np.random.random((5,5))
Zmax, Zmin = Z.max(), Z.min()
Z = (Z - Zmin)/(Zmax - Zmin)
print(Z)
Ruby:
z = Numo::DFloat.new(5,5).rand
zmin, zmax = z.minmax
z = (z - zmin)/(zmax - zmin)
p z
Python:
color = np.dtype([("r", np.ubyte, 1),
("g", np.ubyte, 1),
("b", np.ubyte, 1),
("a", np.ubyte, 1)])
Ruby:
# todo: record
color = Numo::Struct.new do
uint8 "r"
uint8 "g"
uint8 "b"
uint8 "a"
end
Python:
Z = np.dot(np.ones((5,3)), np.ones((3,2)))
print(Z)
Ruby:
x = Numo::DFloat.ones(5,3)
y = Numo::DFloat.ones(3,2)
z = x.dot y
p z
Python:
# Author: Evgeni Burovski
Z = np.arange(11)
Z[(3 < Z) & (Z <= 8)] *= -1
print(Z)
Ruby:
z = Numo::Int32.new(11).seq
z[(3 < z) & (z <= 8)] *= -1
p z
Python:
# Author: Jake VanderPlas
print(sum(range(5),-1))
from numpy import *
print(sum(range(5),-1))
Ruby:
p [*0...5,-1].inject(:+)
p Numo::Int32[0...5].sum(-1)
Python:
Z = np.arange(5)
Z**Z
2 << Z >> 2
Z <- Z
1j*Z
Z/1/1
Z<Z>Z
Ruby:
z = Numo::Int32.new(5).seq
z**z
2 << z >> 2
z <- z
1i*z
z/1/1
z<z>z
Python:
print(np.array(0) / np.array(0))
print(np.array(0) // np.array(0))
print(np.array([np.nan]).astype(int).astype(float))
Ruby:
p Numo::Int32[0] / Numo::Int32[0]
p Numo::DFloat[Float::NAN].cast_to(Numo::Int32).cast_to(Numo::DFloat)
Python:
# Author: Charles R Harris
Z = np.random.uniform(-10,+10,10)
print (np.trunc(Z + np.copysign(0.5, Z)))
Ruby:
z = Numo::DFloat.new(10).rand(-10,+10)
p (z + (0.5*z.sign)).trunc
# todo: copysign
Python:
Z1 = np.random.randint(0,10,10)
Z2 = np.random.randint(0,10,10)
print(np.intersect1d(Z1,Z2))
Ruby:
# todo: intersect1d
Python:
A = np.ones(3)*1
B = np.ones(3)*2
np.add(A,B,out=B)
np.divide(A,2,out=A)
np.negative(A,out=A)
np.multiply(A,B,out=A)
Ruby:
a = Numo::DFloat.new(3).fill(1)
b = Numo::DFloat.new(3).fill(2)
p (a+b)*(-a/2)
(a+b.inplace)*(-a.inplace/2)
p b
Python:
Z = np.random.uniform(0,10,10)
print (Z - Z%1)
print (np.floor(Z))
print (np.ceil(Z)-1)
print (Z.astype(int))
print (np.trunc(Z))
Ruby:
z = Numo::DFloat.new(10).rand(10)
p z - z%1
p z.floor
p z.ceil - 1
p z.cast_to(Numo::Int32)
p z.trunc
Python:
Z = np.zeros((5,5))
Z += np.arange(5)
print(Z)
Ruby:
z = Numo::DFloat.zeros(5,5)
z += Numo::Int32.new(5).seq
p z
Python:
Z = np.linspace(0,1,12,endpoint=True)[1:-1]
print(Z)
Ruby:
z = Numo::DFloat.linspace(0,1,12)[1..-2]
p z
Python:
Z = np.random.random(10)
Z.sort()
print(Z)
Ruby:
z = Numo::DFloat.new(10).rand
z = z.sort
p z
Python:
Z = np.zeros(10)
Z.flags.writeable = False
Z[0] = 1
Ruby:
z = Numo::DFloat.zeros(10)
z.freeze
z[0] = 1
Python:
Z = np.random.random((10,2))
X,Y = Z[:,0], Z[:,1]
R = np.sqrt(X**2+Y**2)
T = np.arctan2(Y,X)
print(R)
print(T)
Ruby:
z = Numo::DFloat.new(10,2).rand
x,y = z[true,0], z[true,1]
r = Numo::NMath.sqrt(x**2+y**2)
t = Numo::NMath.atan2(y,x)
p r
p t
Python:
Z = np.random.random(10)
Z[Z.argmax()] = 0
print(Z)
Ruby:
z = Numo::DFloat.new(10).rand
z[z.max_index] = 0
p z
Python:
Z = np.arange(100)
v = np.random.uniform(0,100)
index = (np.abs(Z-v)).argmin()
print(Z[index])
Ruby:
z = Numo::Int32.new(100).seq
v = rand*100
index = (z-v).abs.min_index
p z[index]
Python:
from io import StringIO
# Fake file
s = StringIO("""1, 2, 3, 4, 5\n
6, , , 7, 8\n
, , 9,10,11\n""")
Z = np.genfromtxt(s, delimiter=",", dtype=np.int)
print(Z)
Ruby:
require "stringio"
s = StringIO.new("1, 2, 3, 4, 5
6, , , 7, 8
, , 9,10,11")
z = Numo::NArray[*s.readlines.map{|l| l.split(",").map{|x| x.strip.empty? ? Float::NAN : x.to_f}}]
Python:
Z = np.arange(9).reshape(3,3)
for index, value in np.ndenumerate(Z):
print(index, value)
for index in np.ndindex(Z.shape):
print(index, Z[index])
Ruby:
z = Numo::Int32.new(3,3).seq
z.each_with_index{|x,*i| p [i,x]}
Python:
X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))
D = np.sqrt(X*X+Y*Y)
sigma, mu = 1.0, 0.0
G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )
print(G)
Ruby:
x = Numo::DFloat.linspace(-1,1,10)
y = Numo::DFloat.linspace(-1,1,10).expand_dims(1)
d = Numo::NMath.sqrt(x*x+y*y)
sigma, mu = 1.0, 0.0
g = Numo::NMath.exp(-( (d-mu)**2 / ( 2.0 * sigma**2 ) ) )
p g
Python:
# Author: Warren Weckesser
X = np.random.rand(5, 10)
# Recent versions of numpy
Y = X - X.mean(axis=1, keepdims=True)
# Older versions of numpy
Y = X - X.mean(axis=1).reshape(-1, 1)
print(Y)
Ruby:
x = Numo::DFloat.new(5, 10).rand
y = x - x.mean(1).expand_dims(1)
Python:
# Author: Steve Tjoa
Z = np.random.randint(0,10,(3,3))
print(Z)
print(Z[Z[:,1].argsort()])
Ruby:
z = Numo::Int32.new(3,3).rand(10)
p z
p z[z[true,1].sort_index,true]
Python:
# Author: Warren Weckesser
Z = np.random.randint(0,3,(3,10))
print((~Z.any(axis=0)).any())
Ruby:
z = Numo::Int32.new(3,10).rand(3)
(~z.ne(0).any?(0)).any?
Python:
Z = np.random.uniform(0,1,10)
z = 0.5
m = Z.flat[np.abs(Z - z).argmin()]
print(m)
Ruby:
z = Numo::DFloat.new(10).rand
x = 0.5
m = z[(z - x).abs.min_index]
p m
Python:
# Author: Nadav Horesh
w,h = 16,16
I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte)
F = I[...,0]*256*256 + I[...,1]*256 +I[...,2]
n = len(np.unique(F))
print(np.unique(I))
Ruby:
# todo: unique
w,h = 16,16
i = Numo::UInt32.new(h,w,3).rand(2)
f = i[false,0]*256*256 + i[false,1]*256 +i[false,2]
p f.flatten.sort.to_a.uniq
Python:
A = np.random.randint(0,10,(3,4,3,4))
sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)
print(sum)
Ruby:
a = Numo::Int32.new(3,4,3,4).rand(10)
sum = a.sum(-2,-1)
p sum
Python:
# Author: Jaime Fernández del Río
D = np.random.uniform(0,1,100)
S = np.random.randint(0,10,100)
D_sums = np.bincount(S, weights=D)
D_counts = np.bincount(S)
D_means = D_sums / D_counts
print(D_means)
Ruby:
# todo: bincount
Python:
# Author: Mathieu Blondel
A = np.random.uniform(0,1,(5,5))
B = np.random.uniform(0,1,(5,5))
# Slow version
np.diag(np.dot(A, B))
# Fast version
np.sum(A * B.T, axis=1)
# Faster version
np.einsum("ij,ji->i", A, B)
Ruby:
a = Numo::DFloat.new(3,3).seq
b = Numo::DFloat.new(3,3).seq
p a.mulsum(b.transpose,1)
# speed?
Python:
# Author: Warren Weckesser
Z = np.array([1,2,3,4,5])
nz = 3
Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))
Z0[::nz+1] = Z
print(Z0)
Ruby:
z = Numo::NArray[1,2,3,4,5]
nz = 3
z0 = Numo::Int32.zeros(z.size + (z.size-1)*(nz))
# todo: rangewithstep
# z0[(0..-1).step(nz+1)] = z
# p z0
Python:
A = np.ones((5,5,3))
B = 2*np.ones((5,5))
print(A * B[:,:,None])
Ruby:
a = Numo::Int32.ones(5,5,3)
b = Numo::Int32.new(5,5).fill(2)
p a * b[:*,:*,:-]
Python:
# Author: Eelco Hoogendoorn
A = np.arange(25).reshape(5,5)
A[[0,1]] = A[[1,0]]
print(A)
Ruby:
a = Numo::Int32.new(5,5).seq
a[[0,1],true] = a[[1,0],true].copy
p a
# todo: identity check between read/write array
Python:
# Author: Nicolas P. Rougier
faces = np.random.randint(0,100,(10,3))
F = np.roll(faces.repeat(2,axis=1),-1,axis=1)
F = F.reshape(len(F)*3,2)
F = np.sort(F,axis=1)
G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] )
G = np.unique(G)
print(G)
Ruby:
# todo: roll
Python:
# Author: Jaime Fernández del Río
C = np.bincount([1,1,2,3,4,4,6])
A = np.repeat(np.arange(len(C)), C)
print(A)
Ruby:
# todo: bincount, repeat
Python:
# Author: Jaime Fernández del Río
def moving_average(a, n=3) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
Z = np.arange(20)
print(moving_average(Z, n=3))
Ruby:
def moving_average(a, n=3)
ret = a.cumsum
ret[n..-1] = ret[n..-1] - ret[0..-n-1]
ret[n-1..-1] / n
end
z = Numo::DFloat.new(20).seq
p moving_average(z, 3)
Python:
# Author: Joe Kington / Erik Rigtorp
from numpy.lib import stride_tricks
def rolling(a, window):
shape = (a.size - window + 1, window)
strides = (a.itemsize, a.itemsize)
return stride_tricks.as_strided(a, shape=shape, strides=strides)
Z = rolling(np.arange(10), 3)
print(Z)
Ruby:
# no module: stride_tricks
Python:
# Author: Nathaniel J. Smith
Z = np.random.randint(0,2,100)
np.logical_not(Z, out=Z)
Z = np.random.uniform(-1.0,1.0,100)
np.negative(Z, out=Z)
Ruby:
# todo: logical_not
z = Numo::Int32.new(100).rand(2)
p z
z.inplace ^ 1
p z
z = Numo::DFloat.new(100).rand(-1,1)
p z
-z.inplace
p z
Python:
def distance(P0, P1, p):
T = P1 - P0
L = (T**2).sum(axis=1)
U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L
U = U.reshape(len(U),1)
D = P0 + U*T - p
return np.sqrt((D**2).sum(axis=1))
P0 = np.random.uniform(-10,10,(10,2))
P1 = np.random.uniform(-10,10,(10,2))
p = np.random.uniform(-10,10,( 1,2))
print(distance(P0, P1, p))
Ruby:
def distance(p0, p1, p)
t = p1 - p0
l = (t**2).sum(1)
u = -((p0[true,0]-p[false,0])*t[true,0] + (p0[true,1]-p[false,1])*t[true,1]) / l
u = u.reshape(u.size,1)
d = p0 + u*t - p
return Numo::NMath.sqrt((d**2).sum(1))
end
p0 = Numo::DFloat.new(10,2).rand(-10,10)
p1 = Numo::DFloat.new(10,2).rand(-10,10)
p = Numo::DFloat.new( 1,2).rand(-10,10)
p distance(p0, p1, p)
Python:
# Author: Italmassov Kuanysh
# based on distance function from previous question
P0 = np.random.uniform(-10, 10, (10,2))
P1 = np.random.uniform(-10,10,(10,2))
p = np.random.uniform(-10, 10, (10,2))
print(np.array([distance(P0,P1,p_i) for p_i in p]))
Ruby:
p0 = Numo::DFloat.new(10,2).rand(-10,10)
p1 = Numo::DFloat.new(10,2).rand(-10,10)
p = Numo::DFloat.new(10,2).rand(-10,10)
p a = p.shape[0].times.map{|i| distance(p0, p1, p.slice(i,true))}
# todo: concat narray
fill value when necessary) (★★★)¶Python:
# Author: Nicolas Rougier
Z = np.random.randint(0,10,(10,10))
shape = (5,5)
fill = 0
position = (1,1)
R = np.ones(shape, dtype=Z.dtype)*fill
P = np.array(list(position)).astype(int)
Rs = np.array(list(R.shape)).astype(int)
Zs = np.array(list(Z.shape)).astype(int)
R_start = np.zeros((len(shape),)).astype(int)
R_stop = np.array(list(shape)).astype(int)
Z_start = (P-Rs//2)
Z_stop = (P+Rs//2)+Rs%2
R_start = (R_start - np.minimum(Z_start,0)).tolist()
Z_start = (np.maximum(Z_start,0)).tolist()
R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist()
Z_stop = (np.minimum(Z_stop,Zs)).tolist()
r = [slice(start,stop) for start,stop in zip(R_start,R_stop)]
z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)]
R[r] = Z[z]
print(Z)
print(R)
Ruby:
# todo: minimum, maximum
Python:
# Author: Stefan van der Walt
Z = np.arange(1,15,dtype=np.uint32)
R = stride_tricks.as_strided(Z,(11,4),(4,4))
print(R)
Ruby:
# no moudle: stride_tricks
Python:
# Author: Stefan van der Walt
Z = np.random.uniform(0,1,(10,10))
U, S, V = np.linalg.svd(Z) # Singular Value Decomposition
rank = np.sum(S > 1e-10)
print(rank)
Ruby:
# todo: svd
Python:
Z = np.random.randint(0,10,50)
print(np.bincount(Z).argmax())
Ruby:
# todo: bincount
Python:
# Author: Chris Barker
Z = np.random.randint(0,5,(10,10))
n = 3
i = 1 + (Z.shape[0]-3)
j = 1 + (Z.shape[1]-3)
C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides)
print(C)
Ruby:
# no module: stride_tricks
Python:
# Author: Eric O. Lebigot
# Note: only works for 2d array and value setting using indices
class Symetric(np.ndarray):
def __setitem__(self, index, value):
i,j = index
super(Symetric, self).__setitem__((i,j), value)
super(Symetric, self).__setitem__((j,i), value)
def symetric(Z):
return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)
S = symetric(np.random.randint(0,10,(5,5)))
S[2,3] = 42
print(S)
Ruby:
module Symetric
def []=(i,j,value)
super(i,j,value)
super(j,i,value) if i != j
end
end
def symetric(z)
y = z + z.transpose
y.diagonal.store(z.diagonal)
y.extend(Symetric)
end
s = symetric(Numo::Int32.new(5,5).rand(10))
s[2,3] = 42
p s
Python:
# Author: Stefan van der Walt
p, n = 10, 20
M = np.ones((p,n,n))
V = np.ones((p,n,1))
S = np.tensordot(M, V, axes=[[0, 2], [0, 1]])
print(S)
# It works, because:
# M is (p,n,n)
# V is (p,n,1)
# Thus, summing over the paired axes 0 and 0 (of M and V independently),
# and 2 and 1, to remain with a (n,1) vector.
Ruby:
p, n = 10, 20
m = Numo::DFloat.ones(p,n,n)
v = Numo::DFloat.ones(p,n,1)
s = m.transpose(0,2,1).mulsum(v,0,1)
p s
# todo: tensordot?
Python:
# Author: Robert Kern
Z = np.ones((16,16))
k = 4
S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0),
np.arange(0, Z.shape[1], k), axis=1)
print(S)
Ruby:
n, k = 16, 4
z = Numo::DFloat.ones(n,n)
s = z.reshape(n/k,k,n/k,k).sum(1,3)
# todo: reduceat?
Python:
# Author: Nicolas Rougier
def iterate(Z):
# Count neighbours
N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] +
Z[1:-1,0:-2] + Z[1:-1,2:] +
Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:])
# Apply rules
birth = (N==3) & (Z[1:-1,1:-1]==0)
survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1)
Z[...] = 0
Z[1:-1,1:-1][birth | survive] = 1
return Z
Z = np.random.randint(0,2,(50,50))
for i in range(100): Z = iterate(Z)
print(Z)
Ruby:
def iterate(z)
# Count neighbours
n = z[0..-3,0..-3] + z[0..-3,1..-2] + z[0..-3,2..-1] +
z[1..-2,0..-3] + z[1..-2,2..-1] +
z[2..-1,0..-3] + z[2..-1,1..-2] + z[2..-1,2..-1]
# Apply rules
birth = n.eq(3) & z[1..-2,1..-2].eq(0)
survive = (n.eq(2) | n.eq(3)) & z[1..-2,1..-2].eq(1)
z[] = 0
#z[1..-2,1..-2][birth | survive] = 1
y = z[0..-3,0..-3].copy
y[birth | survive] = 1
z[1..-2,1..-2] = y
end
z = Numo::Int32.new(50,50).rand(2)
100.times{ iterate(z) }
p z
Python:
Z = np.arange(10000)
np.random.shuffle(Z)
n = 5
# Slow
print (Z[np.argsort(Z)[-n:]])
# Fast
print (Z[np.argpartition(-Z,n)[:n]])
Ruby:
z = Numo::DFloat.new(10000).rand
n = 5
p z[z.sort_index[-n..-1]]
# todo: shuffle, argpartition
Python:
# Author: Stefan Van der Walt
def cartesian(arrays):
arrays = [np.asarray(a) for a in arrays]
shape = (len(x) for x in arrays)
ix = np.indices(shape, dtype=int)
ix = ix.reshape(len(arrays), -1).T
for n, arr in enumerate(arrays):
ix[:, n] = arrays[n][ix[:, n]]
return ix
print (cartesian(([1, 2, 3], [4, 5], [6, 7])))
Ruby:
def cartesian(*arrays)
arrays = arrays.map{|a| Numo::Int32.cast(a)}
shape = arrays.map{|x| x.size}
asz = arrays.size
ix = Numo::Int32.zeros(*shape, asz)
arrays.each_with_index do |arr,n|
s = [1]*asz
s[n] = arr.size
ix[false,n] = arr.reshape(*s)
end
return ix.reshape(ix.size/asz,asz)
end
p cartesian([1, 2, 3], [4, 5], [6, 7])
Python:
Z = np.array([("Hello", 2.5, 3),
("World", 3.6, 2)])
R = np.core.records.fromarrays(Z.T,
names='col1, col2, col3',
formats = 'S8, f8, i8')
print(R)
Ruby:
# todo: record
Python:
# Author: Ryan G.
x = np.random.rand(5e7)
%timeit np.power(x,3)
%timeit x*x*x
%timeit np.einsum('i,i,i->i',x,x,x)
Ruby:
x = Numo::DFloat.new(5e7).rand
x**3 # probably fast
Python:
# Author: Gabe Schwartz
A = np.random.randint(0,5,(8,3))
B = np.random.randint(0,5,(2,2))
C = (A[..., np.newaxis, np.newaxis] == B)
rows = (C.sum(axis=(1,2,3)) >= B.shape[1]).nonzero()[0]
print(rows)
Ruby:
a = Numo::Int32.new(8,3).rand(5)
b = Numo::Int32.new(2,2).rand(5)
c = a[false,:new,:new].eq b
rows = (c.count_true(1,2,3) >= b.shape[1]).where
p rows
Python:
# Author: Robert Kern
Z = np.random.randint(0,5,(10,3))
E = np.logical_and.reduce(Z[:,1:] == Z[:,:-1], axis=1)
U = Z[~E]
print(Z)
print(U)
Ruby:
z = Numo::Int32.new(10,3).rand(5)
e = (z[true,1..-1].eq z[true,0..-2]).all?(1)
u = z[(~e).where,true]
p z
p u
Python:
# Author: Warren Weckesser
I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])
B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)
print(B[:,::-1])
# Author: Daniel T. McDonald
I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)
print(np.unpackbits(I[:, np.newaxis], axis=1))
Ruby:
# todo: bit
Python:
# Author: Jaime Fernández del Río
Z = np.random.randint(0,2,(6,3))
T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))
_, idx = np.unique(T, return_index=True)
uZ = Z[idx]
print(uZ)
Ruby:
# todo: unique row
Python:
# Author: Alex Riley
# Make sure to read: http://ajcr.net/Basic-guide-to-einsum/
A = np.random.uniform(0,1,10)
B = np.random.uniform(0,1,10)
np.einsum('i->', A) # np.sum(A)
np.einsum('i,i->i', A, B) # A * B
np.einsum('i,i', A, B) # np.inner(A, B)
np.einsum('i,j', A, B) # np.outer(A, B)
Ruby:
# no method: einsum
a = Numo::DFloat.new(10).rand(0,1)
b = Numo::DFloat.new(10).rand(0,1)
a.sum # np.sum(A)
a*b # A * B
a.mulsum(b) # np.inner(A, B)
a[false,:new]*b # np.outer(A, B)
Python:
# Author: Bas Swinckels
phi = np.arange(0, 10*np.pi, 0.1)
a = 1
x = a*phi*np.cos(phi)
y = a*phi*np.sin(phi)
dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths
r = np.zeros_like(x)
r[1:] = np.cumsum(dr) # integrate path
r_int = np.linspace(0, r.max(), 200) # regular spaced path
x_int = np.interp(r_int, r, x) # integrate path
y_int = np.interp(r_int, r, y)
Ruby:
# todo: interp
Python:
# Author: Evgeni Burovski
X = np.asarray([[1.0, 0.0, 3.0, 8.0],
[2.0, 0.0, 1.0, 1.0],
[1.5, 2.5, 1.0, 0.0]])
n = 4
M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)
M &= (X.sum(axis=-1) == n)
print(X[M])
Ruby:
Python:
# Author: Jessica B. Hamrick
X = np.random.randn(100) # random 1D array
N = 1000 # number of bootstrap samples
idx = np.random.randint(0, X.size, (N, X.size))
means = X[idx].mean(1)
confint = np.percentile(means, [2.5, 97.5])
print(confint)
Ruby:
x = Numo::DFloat.new(100).rand
n = 1000 # number of bootstrap samples
idx = Numo::Int32.new(n, x.size).rand(x.size)
means = x[idx].mean(1)
confint = means[means.sort_index[means.size/100.0*Numo::DFloat[2.5, 97.5]]]
p confint
# todo: percentile, rand_norm