import numpy as np
x = np.array([[1,1,1],[2,2,2],[3,3,3]])
xt = np.array([1,2,3])
L = len(xt)
for i in range(0,L):
s = x-xt[i]
is there another way to get the same results without the use of a for loop, thanks.
Related
How to execute this code:
import numpy as np
import math
x = np.arange(1,9, 0.5)
k = math.cos(x)
print(x)
I got an error like this:
TypeError: only size-1 arrays can be converted to Python scalars
Thank you in advance.
So this is happening because math.cos doesn't accept numpy arrays larger than size 1. That's why if you had a np array of size 1, your approach would still work.
A simpler way you can achieve the result is to use np.cos(x) directly:
import numpy as np
x = np.arange(1,9, 0.5)
k = np.cos(x)
print(x)
print(k)
If you have to use the math module, you can try iterating through the array and applying math.cos to each member of the array:
import numpy as np
import math
x = np.arange(1,9,0.5)
for item in x:
k = math.cos(item)
print(k) # or add to a new array/list
You're looking for something like this?
import numpy as np
import math
x = np.arange(1,9, 0.5)
for ang in x:
k = math.cos(ang)
print(k)
You are trying to pass ndarray (returned by arange) to a function, which expects just real number. Use np.cos instead.
If you want pure-Python:
You can use math.fun in map like below:
import math
x = range(1,9)
print(list(map(math.cos, x)))
Output:
[0.5403023058681398, -0.4161468365471424, -0.9899924966004454, -0.6536436208636119, 0.2836621854632263, 0.9601702866503661, 0.7539022543433046, -0.14550003380861354]
I have a numpy array of booleans:
import numpy as np
x = np.zeros(100).astype(np.bool)
x[20] = True # say
When I try to insert this (one element per document) as part of an OrderedDict into mongodb, I get the following error:
InvalidDocument: cannot encode object: False, of type: <class 'numpy.bool_'>
This is a serialization issue I have encountered before for singleton numpy booleans.
How do I convert the numpy array into an array of python booleans for serialization?
The following did not work:
y = x.astype(bool)
You can use numpy.ndarray.tolist here.
import numpy as np
x = np.zeros(100).astype(np.bool)
y = x.tolist()
print(type(x))
# numpy.ndarray
print(type(x[0]))
# numpy.bool_
print(type(y))
# list
print(type(y[0]))
# bool
You can try numpy.asscalar
import numpy as np
x = np.zeros(100).astype(np.bool)
z = [np.asscalar(x_i) for x_i in x]
print(type(z))
You can also use item() which is a better option since asscalar is depreceted.
import numpy as np
x = np.zeros(100).astype(np.bool)
z = [x_i.item() for x_i in x]
print(type(z))
print(z)
For a longer list, tolist() is better option.
import numpy as np
import time
x = np.zeros(100000).astype(np.bool)
t1 = time.time()
z = [x_i.item() for x_i in x]
t2 = time.time()
print(t2-t1)
t1 = time.time()
z = x.tolist()
t2 = time.time()
print(t2-t1)
0.0519254207611084
0.0015206336975097656
So, I have just this week come across a solution to this (albeit my own) question from two years ago... Thanks SO!
I am going to invoke the brilliant numpyencoder (https://pypi.org/project/numpyencoder) as follows:
# Set up the problem
import numpy as np
x = np.zeros(100).astype(bool) # Note: bool <- np.bool is now deprecated!
x[20] = True
# Let's roll
import json
from numpyencoder import NumpyEncoder
sanitized_json_string = json.dumps(x, cls=NumpyEncoder)
# One could stop there since the payload is now ready to go - but just to confirm:
x_sanitized=json.loads(sanitized_json_string)
print(x_sanitized)
This feels like it should be a simple problem but I am newer to python, in R i would use a foreach loop that gave me an option to combine.
I have tried a for loop that lets me print out all the values i need but i want them collected into a vector of values that i can use later.
from scipy.stats import gamma
import scipy.stats as stats
import numpy as np
import random
data2 = np.random.gamma(1,2, size = 500)
gammT = np.log(data2 + 1)
mean = np.mean(gammT)
sd = np.std(gammT)
a = (mean/ sd)**2
b = (sd**2)/ mean
for i in range(1,100):
gammT = random.sample(list(gammT), 500)
gamm = np.random.gamma(a,b, size = len(gammT))
s = stats.anderson_ksamp([gammT,gamm])
s = s[2]
print(s)
So i am able to print all the values i want but i want them all to be gathered together in a vector of values. I have tried to append and make lists but am not able to get them together.
from scipy.stats import gamma
import scipy.stats as stats
import numpy as np
import random
gammT = np.log(data2.iScore + 1)
mean = np.mean(gammT)
sd = np.std(gammT)
a = (mean/ sd)**2
b = (sd**2)/ mean
#initialize empty list
result=[]
for i in range(100):
# removed (1,100) you only need range(100) for 100 elements
gammT = random.sample(list(gammT), 500)
gamm = np.random.gamma(a,b, size = len(gammT))
s = stats.anderson_ksamp([gammT,gamm])
s = s[2]
#append calculation to list
result.append(s)
print(s)
print(result)
I want to parallelize a function using numba.vectorize, but my function doesn't take any input. Currently, I use a dummy array and dummy input for my function that is never used.
Is there a more elegant/fast way (possibly without using numba.vectorize)?
Code example (not my actual code, only for demonstration how I discard input):
import numpy as np
from numba import vectorize
#vectorize(["int32(int32)"], nopython=True)
def particle_path(discard_me):
x = 0
for _ in range(10):
x += np.random.uniform(0, 1)
return np.int32(x)
arr = particle_path(np.empty(1024, dtype=np.int32))
print(arr)
If you'll simply be dealing with 1D arrays, then you can use the following, where the array must be instantiated outside the function. There doesn't seem to be any reason to use vectorize here, you can achieve the goal simply with jit although you do have to explicitly write the loop over the array elements using this. If your array will always be 1D, then you can use:
import numpy as np
from numba import jit
#jit(nopython=True)
def particle_path(out):
for i in range(len(out)):
x = 0
for _ in range(10):
x += np.random.uniform(0, 1)
out[i] = x
arr = np.empty(1024, dtype=np.int32)
particle_path(arr)
You can similarly deal with any-dimensional arrays using the flat attribute (and make sure to use .size to get total number of elements in the array):
#jit(nopython=True)
def particle_path(out):
for i in range(out.size):
x = 0
for _ in range(10):
x += np.random.uniform(0, 1)
out.flat[i] = x
arr = np.empty(1024, dtype=np.int32)
particle_path(arr)
and finally you can create your array inside your function if you need a new array each time you run the function (use the above instead if you'll be calling the function repeatedly and want to overwrite the same array, hence saving the time to re-allocate the same array over and over again).
#jit(nopython=True)
def particle_path(num):
out = np.empty(shape=num, dtype=np.int32)
for i in range(num):
x = 0
for _ in range(10):
x += np.random.uniform(0, 1)
out[i] = x
return out
arr = particle_path(1024)
I try to multiply two matrices in a python program using Keras.
import keras.backend as K
import numpy as np
A = np.random.rand(10,500)
B = np.random.rand(500,6000)
x = K.placeholder(shape=A.shape)
y = K.placeholder(shape=B.shape)
xy = K.dot(x, y)
xy.eval(A,B)
I know this cannot work, but I also don't know how I can make it work.
You need to use a variable instead of a place holder.
import keras.backend as K
import numpy as np
A = np.random.rand(10,500)
B = np.random.rand(500,6000)
x = K.variable(value=A)
y = K.variable(value=B)
z = K.dot(x,y)
# Here you need to use K.eval() instead of z.eval() because this uses the backend session
K.eval(z)