scipy

问题 In order to understand what I'm trying to achieve let's imagine an ndarray a with shape (8,8,8) from which I lexicographically take blocks of shape (4,4,4) . So while iterating through such blocks the indexes would look as follows: 0: a[0:4, 0:4, 0:4] 1: a[0:4, 0:4, 4:8] 2: a[0:4, 4:8, 0:4] 3: a[0:4, 4:8, 4:8] 4: a[4:8, 0:4, 0:4] 5: a[4:8, 0:4, 4:8] 6: a[4:8, 4:8, 0:4] 7: a[4:8, 4:8, 4:8] It is these blocks of data which I'm trying to access. Obviously, this can be described by using an

问题 In order to understand what I'm trying to achieve let's imagine an ndarray a with shape (8,8,8) from which I lexicographically take blocks of shape (4,4,4) . So while iterating through such blocks the indexes would look as follows: 0: a[0:4, 0:4, 0:4] 1: a[0:4, 0:4, 4:8] 2: a[0:4, 4:8, 0:4] 3: a[0:4, 4:8, 4:8] 4: a[4:8, 0:4, 0:4] 5: a[4:8, 0:4, 4:8] 6: a[4:8, 4:8, 0:4] 7: a[4:8, 4:8, 4:8] It is these blocks of data which I'm trying to access. Obviously, this can be described by using an

问题 In order to understand what I'm trying to achieve let's imagine an ndarray a with shape (8,8,8) from which I lexicographically take blocks of shape (4,4,4) . So while iterating through such blocks the indexes would look as follows: 0: a[0:4, 0:4, 0:4] 1: a[0:4, 0:4, 4:8] 2: a[0:4, 4:8, 0:4] 3: a[0:4, 4:8, 4:8] 4: a[4:8, 0:4, 0:4] 5: a[4:8, 0:4, 4:8] 6: a[4:8, 4:8, 0:4] 7: a[4:8, 4:8, 4:8] It is these blocks of data which I'm trying to access. Obviously, this can be described by using an

问题 I want to calculate the n-th odd root of some numbers in python. Numpy as a cube root function. Using that function I can compute x^(1/3). x = np.linspace(-100,100,100) np.cbrt(x) >>> array([-4.64158883, -4.26859722, -3.81571414, -3.21829795, -2.23144317, 2.23144317, 3.21829795, 3.81571414, 4.26859722, 4.64158883]) However, if I want to compute the same thing for other k-th odd roots in a straightforward manner I'm somewhat stuck. I cannot use np.power directly, not even to compute the cube

问题 I wanted to manually code a 1D convolution because I was playing around with kernels for time series classification, and I decided to make the famous Wikipedia convolution image, as seen here. Here's my script. I'm using the standard formula for convolution for a digital signal. import numpy as np import matplotlib.pyplot as plt import scipy.ndimage plt.style.use('ggplot') def convolve1d(signal, ir): """ we use the 'same' / 'constant' method for zero padding. """ n = len(signal) m = len(ir)

问题 I wanted to manually code a 1D convolution because I was playing around with kernels for time series classification, and I decided to make the famous Wikipedia convolution image, as seen here. Here's my script. I'm using the standard formula for convolution for a digital signal. import numpy as np import matplotlib.pyplot as plt import scipy.ndimage plt.style.use('ggplot') def convolve1d(signal, ir): """ we use the 'same' / 'constant' method for zero padding. """ n = len(signal) m = len(ir)

问题 I wanted to manually code a 1D convolution because I was playing around with kernels for time series classification, and I decided to make the famous Wikipedia convolution image, as seen here. Here's my script. I'm using the standard formula for convolution for a digital signal. import numpy as np import matplotlib.pyplot as plt import scipy.ndimage plt.style.use('ggplot') def convolve1d(signal, ir): """ we use the 'same' / 'constant' method for zero padding. """ n = len(signal) m = len(ir)

问题 I wanted to manually code a 1D convolution because I was playing around with kernels for time series classification, and I decided to make the famous Wikipedia convolution image, as seen here. Here's my script. I'm using the standard formula for convolution for a digital signal. import numpy as np import matplotlib.pyplot as plt import scipy.ndimage plt.style.use('ggplot') def convolve1d(signal, ir): """ we use the 'same' / 'constant' method for zero padding. """ n = len(signal) m = len(ir)

问题 def tdm_modify(feature_names,tdm): non_useful_words=['kill','stampede','trigger','cause','death','hospital'\ ,'minister','said','told','say','injury','victim','report'] indexes=[feature_names.index(word) for word in non_useful_words] for index in indexes: tdm[:,index]=0 return tdm I want to manually set zero weights for some terms in tdm matrix. Using the above code I get the warning. I don't seem to understand why? Is there a better way to do this? C:\Anaconda\lib\site-packages\scipy\sparse

问题 def tdm_modify(feature_names,tdm): non_useful_words=['kill','stampede','trigger','cause','death','hospital'\ ,'minister','said','told','say','injury','victim','report'] indexes=[feature_names.index(word) for word in non_useful_words] for index in indexes: tdm[:,index]=0 return tdm I want to manually set zero weights for some terms in tdm matrix. Using the above code I get the warning. I don't seem to understand why? Is there a better way to do this? C:\Anaconda\lib\site-packages\scipy\sparse