numpy

问题 I am using the following code to calculate the quartiles of a given data set: #!/usr/bin/python import numpy as np series = [1,2,2,2,2,2,2,2,2,2,2,5,5,6,7,8] p1 = 25 p2 = 50 p3 = 75 q1 = np.percentile(series, p1) q2 = np.percentile(series, p2) q3 = np.percentile(series, p3) print('percentile(' + str(p1) + '): ' + str(q1)) print('percentile(' + str(p2) + '): ' + str(q2)) print('percentile(' + str(p3) + '): ' + str(q3)) The percentile function returns the quartiles, however, I would also like

问题 I am using the following code to calculate the quartiles of a given data set: #!/usr/bin/python import numpy as np series = [1,2,2,2,2,2,2,2,2,2,2,5,5,6,7,8] p1 = 25 p2 = 50 p3 = 75 q1 = np.percentile(series, p1) q2 = np.percentile(series, p2) q3 = np.percentile(series, p3) print('percentile(' + str(p1) + '): ' + str(q1)) print('percentile(' + str(p2) + '): ' + str(q2)) print('percentile(' + str(p3) + '): ' + str(q3)) The percentile function returns the quartiles, however, I would also like

问题 Suppose I have an (m x n) 2-d numpy array that are just 0's and 1's. I want to "smooth" the array by running, for example, a 3x3 kernel over the array and taking the majority value within that kernel. For values at the edges, I would just ignore the "missing" values. For example, let's say the array looked like import numpy as np x = np.array([[1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1,

问题 Suppose I have an (m x n) 2-d numpy array that are just 0's and 1's. I want to "smooth" the array by running, for example, a 3x3 kernel over the array and taking the majority value within that kernel. For values at the edges, I would just ignore the "missing" values. For example, let's say the array looked like import numpy as np x = np.array([[1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1,

问题 Suppose I have an (m x n) 2-d numpy array that are just 0's and 1's. I want to "smooth" the array by running, for example, a 3x3 kernel over the array and taking the majority value within that kernel. For values at the edges, I would just ignore the "missing" values. For example, let's say the array looked like import numpy as np x = np.array([[1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1,

问题 I'm trying to get all eigenvalues from a 3x3 matrix by using Power Method in Python. However my method returns diffrent eigenvalues from the correct ones for some reason. My matrix: A = [[1, 2, 3], [2, 4, 5], [3, 5,-1]] Correct eigenvalues: [ 8.54851285, -4.57408723, 0.02557437 ] Eigenvalues returned by my method: [ 8.5485128481521926, 4.5740872291939381, 9.148174458392436 ] So the first one is correct, second one has wrong sign and the third one is all wrong. I don't know what I'm doing

问题 There seems to be no equivalent for cosd , sind in sympy (ie cosine and sine for arguments in degrees). Is there any simple way to implement those functions ? For numpy, I did : numpy.cosd = lambda x : numpy.cos( numpy.deg2rad(x) ) Something like : sympy.cosd = lambda x : sympy.cos( sympy.pi/180*x ) works for evaluation, but the expression is printed as : cos(pi*x/180) which is not great for readability (I have complicated expression due to 3D coordinates changes). Is there any way to create

问题 I have processed and saved a large dataset of video and audio file (about 8 to 9 GB of data) The data is saved as 2 numpy arrays, one for each modality Shapes of the files are (number_of_examples, maximum_time_length, feature_length) I want to use this data for training my Neural Network for a classification task I am using the TensorFlow 2.0 Beta version I am running all the codes on Google Colab (after installing tf-2.0 beta) Each time I loading the data in tf.data the entire RAM of the

问题 TL;DR Question Regarding numpy arrays that are neighter C or F contiguous (array's c_contiguous and f_contiguous flags are False): Can an array really be neither C or F contiguous? Or falsy flags just mean numpy can't figure out the correct contigious type? What are the performance implications on such arrays? Are there any optimizations we miss when staying in this state? An array for example: import numpy as np arr = np.random.randint(0, 255, (1000, 1000, 3), dtype='uint8') arr = arr[:, :,

问题 TL;DR Question Regarding numpy arrays that are neighter C or F contiguous (array's c_contiguous and f_contiguous flags are False): Can an array really be neither C or F contiguous? Or falsy flags just mean numpy can't figure out the correct contigious type? What are the performance implications on such arrays? Are there any optimizations we miss when staying in this state? An array for example: import numpy as np arr = np.random.randint(0, 255, (1000, 1000, 3), dtype='uint8') arr = arr[:, :,