print("矩阵a1:\n",a1) print("number of dimension:",a1.ndim) print("shape:",a1.shape) print('size:',a1.size) # matrix 方式创建 a3 = np.matrix([[1,2,3], [4,5,6]]) print("a3:\n",a3) # mat方式 from numpy import * data1 = mat(zeros((3,3)))#创建一个3*3的零矩...
一个n x n matrix 有 math.ceil(n / 2) 个 layers 从第0个layer(最外层)来表示 我来表示每层的方法: 四个值怎么换(rotate/swap) from math import ceil def rotateMatrix(matrix): # dimension for a n by n matrix n = len(matrix) # e.g. if it has 2 layers we have layer number 0,1...
>>> a = arange(6) #1d array >>>printa [012345] >>> >>> b = arange(12).reshape(4,3) #2d array >>>printb[[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]]>>> >>> c = arange(24).reshape(2,3,4) #3d array >>>printc[[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10...
from skimage.feature import hogfrom skimage import exposureimage = rgb2gray(imread('../images/cameraman.jpg'))fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=True) print(image.shape, len(fd))# ((256L, 256L), 2048)fig, (a...
im.convert(mode,matrix)⇒ image 使用转换矩阵将一个“RGB”图像转换为“L”或者“RGB”图像。变量matrix为4或者16元组。 代码语言:javascript 复制 fromPILimportImage im=Image.open("E:\mywife.jpg")print(im.mode)rgb2xyz=(0.412453,0.357580,0.180423,0,0.212671,0.715160,0.072169,0,0.019334,0.119193,0.95...
print("dimension of diabetes data: {}".format(diabetes.shape)) 糖尿病数据的维度:(768,9) “结果”是我们要预测的特征,0表示没有糖尿病,1表示糖尿病。在这768个数据点中,有500个被标记为0,而268被标记为1: print(diabetes.groupby('Outcome').size()) ...
from numpy import * a = array([2,3,4]) print(a) # [2 3 4] print(a.dtype) # int32 b = array([1.0, 2.0, 3.0]) print(b.dtype) # float64 1. 2. 3. 4. 5. 6. 7. 数组将序列包含序列转化成二维的数组,序列包含序列包含序列转化成三维数组等等 b = array( [ (1.5,2,3), (...
print('The dimension of array is:',array.ndim) #二维数组维数(width*height) #python中用numpy.array([1,2,3])创建的"矩阵"被认为是一维的,即不当成1*dim这样的形式 print('The shape of array:',array.shape) print('The size (total number of elements) of array is ',array.size)#所有元素个...
""" X: matrix of dimensions n x indim y: column vector of dimension n x 1 """ # choose random center vectors from training set rnd_idx = random.permutation(X.shape[0])[:self.numCenters] self.centers = [X[i,:] for i in rnd_idx] print "center", self.centers # calcul...
array VS matrix 官方建议多使用array The main advantage of numpy arrays is that they are more general than 2-dimensional matrices. What happens when you want a 3-dimensional array? Then you have to use an ndarray, not a matrix object. Thus, learning to use matrix objects is more work --...