differentiation

问题 I'm trying to make a python proram to find derivatives and integrals as well as showing how. I have so far found that there is an integral_steps function which returns the steps used, but I have not found an equivalent for differentiation. Does anyone know if there is an equivalent? If there isn't, do you have any ideas on how to find the steps needed to find a derivative? 回答1: Method 1 (manual) Looking at the code, the Derivative class is where the top-level logic lives. That's only the top

问题 I'm trying to make a python proram to find derivatives and integrals as well as showing how. I have so far found that there is an integral_steps function which returns the steps used, but I have not found an equivalent for differentiation. Does anyone know if there is an equivalent? If there isn't, do you have any ideas on how to find the steps needed to find a derivative? 回答1: Method 1 (manual) Looking at the code, the Derivative class is where the top-level logic lives. That's only the top

问题 I was looking around trying to find a solution to my issue, the best I could find was this: from sys import platform if platform == "linux" or platform == "linux2": # linux elif platform == "darwin": # OS X elif platform == "win32": # Windows... Does anybody know how I could differentiate a Linux PC from android as android Is based off of Linux. And if this is possible, how could I differentiate Mac OS from iOS 回答1: Use the platform module: import platform print(platform.system()) print

来源： https://stackoverflow.com/questions/54505175/calculating-second-order-derivative-from-timeseries-using-pandas-diff

来源： https://stackoverflow.com/questions/54505175/calculating-second-order-derivative-from-timeseries-using-pandas-diff

问题 I had asked a question and was implementing the solution when I found out that the operation tf.math.count_nonzero does not have gradient defined. So I tried the following round about method: eps = 1e-6 a = tf.ones((4, 4, 2, 2), tf.float32) h = tf.linalg.svd(a, full_matrices=False, compute_uv=False) cond = tf.less(h, eps) h = tf.where(cond, tf.zeros(tf.shape(h)), h) i = tf.reduce_sum(h, axis=-1) j = h[:, :, 0] rank_mat = tf.multiply(2., tf.ones((4, 4))) cond = tf.not_equal(i, j) rank_mat = tf

问题 I had asked a question and was implementing the solution when I found out that the operation tf.math.count_nonzero does not have gradient defined. So I tried the following round about method: eps = 1e-6 a = tf.ones((4, 4, 2, 2), tf.float32) h = tf.linalg.svd(a, full_matrices=False, compute_uv=False) cond = tf.less(h, eps) h = tf.where(cond, tf.zeros(tf.shape(h)), h) i = tf.reduce_sum(h, axis=-1) j = h[:, :, 0] rank_mat = tf.multiply(2., tf.ones((4, 4))) cond = tf.not_equal(i, j) rank_mat = tf

问题 I had asked a question and was implementing the solution when I found out that the operation tf.math.count_nonzero does not have gradient defined. So I tried the following round about method: eps = 1e-6 a = tf.ones((4, 4, 2, 2), tf.float32) h = tf.linalg.svd(a, full_matrices=False, compute_uv=False) cond = tf.less(h, eps) h = tf.where(cond, tf.zeros(tf.shape(h)), h) i = tf.reduce_sum(h, axis=-1) j = h[:, :, 0] rank_mat = tf.multiply(2., tf.ones((4, 4))) cond = tf.not_equal(i, j) rank_mat = tf

问题 I have the following code: syms t x; e=symfun(x-t,[x,t]); In the problem I want to solve x is a function of t but I only know its value at the given t,so I modeled it here as a variable.I want to differentiate e with respect to time without "losing" x,so that I can then substitute it with x'(t) which is known to me. In another question of mine here,someone suggested that I write the following: e=symfun(exp(t)-t,[t]); and after the differentiation check if I can substitute exp(t) with the

问题 I'm trying to calculate Euler-Lagrange equations for a robotic structure. I'll use q to indicate the vector of the joint variables. In my code, I use syms t; q1 = sym('q1(t)'); q2 = sym('q2(t)'); q = [q1, q2]; to declare that q1 and q2 depend on time t . After I calculate the Lagrangian L (in this case it is a simple link with a rotoidal joint) L = (I1z*diff(q1(t), t)^2)/2 + (L1^2*M1*diff(q1(t), t)^2)/8 The problem is that when I try to differentiate L respect to q using diff(L, q) , I get