array

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm engaging in a webgl project. When I call gl.DrawElements, the error 'range out of bounds of buffer' is shown. I surely ensured that I passed correct length or offset of buffer. But, still the error is showing. I think there is several cause that could raise the error. Therefore,I want to ask if you had same problem in your project, what you check for fix this problem? 回答1: There are only 3 reasons you'd get that error when calling gl.drawElements Your indices are referencing vertices out of range of your buffers For example you make a

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: The canonical approach (use of np.vectorize() ) is not working in case of empty array - it ends with IndexError: index 0 is out of bounds for axis 0 with size 0 : >>> def f(x): ... return x + 1 ... >>> F = np.vectorize(f) >>> F(np.array([])) [Traceback removed] IndexError: index 0 is out of bounds for axis 0 with size 0 At the moment I use >>> np.array([f(x) for x in X]) but I am looking for more elegant solution (and efficient). In Python 2 I may go with >>> np.array(map(f, X)) but it fails in Python 3. [EDIT] The question has no answer in

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I know that in numpy I can compute the element-wise minimum of two vectors with numpy.minimum(v1, v2) What if I have a list of vectors of equal dimension, V = [v1, v2, v3, v4] (but a list, not an array)? Taking numpy.minimum(*V) doesn't work. What's the preferred thing to do instead? 回答1: *V works if V has only 2 arrays. np.minimum is a ufunc and takes 2 arguments. As a ufunc it has a .reduce method, so it can apply repeated to a list inputs. In [321]: np.minimum.reduce([np.arange(3), np.arange(2,-1,-1), np.ones((3,))]) Out[321]: array([ 0.,

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: As in title, I need to perform numpy.exp on a very large ndarray, let's say ar , and store the result in ar itself. Can this operation be performed in-place? 回答1: You can use the optional out argument of exp : a = np.array([3.4, 5]) res = np.exp(a, a) print(res is a) print(a) Output: True [ 29.96410005 148.4131591 ] exp(x[, out]) Calculate the exponential of all elements in the input array. Returns out : ndarray Output array, element-wise exponential of x . Here all elements of a will be replaced by the result of exp . The return value res

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: For example I have a PHP array, such as this one <?php $s= array('a','b','c','d','e','f') ; ?> And I need to loop through it in JavaScript, any ideas how do I do that? for ( i=0 ; i < <?php echo sizeof($s) ?> ; i++) { document.write('<?php echo $s [somehow need to get the 'i' value into here] ?>'); } Any suggestions? Thanks! 回答1: <?php $s= array('a','b','c','d','e','f') ; ?> <?php foreach($s as $a){ ?> document.write('<?=$a?>'); <?php } ?> Not tested but thats one way. 回答2: Before your ehco / print or else your php array we make sure it's in

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have a real-time embedded app with the major cycle running at 10KHz. It runs on a TI TMS320C configured to boot from flash. I recently added an initialized array to a source file, and all of a sudden the timing is screwed up (in a way too complex to explain well - essentially a serial port write is no longer completing on time.) The things about this that baffle me: I'm not even accessing the new data , just declaring an initialized array. It is size dependant - the problem only appears if the array is >40 words. I know I'm not overflowing

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I've recently been learning Python multiprocessing, and have run into a roadblock. I have a lerge sparse SciPy array (CSC-format), that I need to share in read only format between 5 worker-processes. I've read this and this (numpy-shared), but this seems to be only for dense-types. How would I share a scipy.sparse.csc_matrix() without copying (or with minimal copying) between 5 multiprocessing Process objects? Even the numpy-shared method seems to require copying the entire array, and even then, I can't just convert a scipy.sparse

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I have a label matrix with dimension (100*100), stored as a numpy array, and I would like to display the matrix with pyglet. My original idea is to use this matrix to form a new pyglet image using function pyglet.image.ImageData(). It requres a buffer of the imagedata as an input, however I have no idea how to get a right formated buffer from the numpy array. Any one have any idea? ps. my current solution: 3d _label = numpy . empty ([ 100 , 100 , 3 ]) 3d _label [:,:, 0 ] = label * 255 # value range of label is [0,1] 3d _label [:,:,

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have an SwiftyJSON object. How to search by key(name) from the array of SwiftyJSON object. For Example : let arrCountry = [JSON]() Now one country array contains { countryid : "1" name : "New York" }, { countryid : "2" name : "Sydeny" } How to search by predicate or something? If I search "y" from the array then it should return both object (Because both contains "y") in another JSON array. And If I type "Syd" then it should return only one object in JSON array. As LIKE query in SQL. And as we are doing with predicates... 回答1: Get the

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I am trying to tackle a classical interview problem which is basically performing a binary search on a list which first increases then decreases. Even though it's obvious that we can achieve O(log n) I couldn't figure out what is wrong the code below that I've written: #include <iostream> using namespace std ; int binarySearch ( int * A , int low , int high , int key ) { while ( low < high ) { int mid = ( low + high ) / 2 ; if ( key < A [ mid ]) { if ( A [ mid - 1 ] < A [ mid ] && A [ mid ] < A [ mid + 1 ]) high = mid - 1 ; else