Tag: x2

Find 3 numbers that are sum up to a given number

Post author By Full Stack
Post date January 16, 2021
No Comments on Find 3 numbers that are sum up to a given number

I need to create a recursive method that takes a number as input that will be the sum and find all the options to add three native numbers so they will be equal to this number. e.g if the input is 5, I will need to find all the 5 options [(1,1,3),(1,2,2),(1,3,1),(2,2,1),(3,1,1)]. max number is […]

Tags "x3"], { "x2": 1, 1, 1)]. max number is 30. the method should print the options, 2, 3, and give back the number of options: public static int solutions(int num) { if(num 30) return 0; return solution, I need to create a recursive method that takes a number as input that will be the sum and find all the options to add three native numbers so, I will need to find all the 5 options [(1, int num) { if(x1 > 10 || x2 > 10 || x3 > 10) return 0; if (x1+x2+x3 != num) { return solutions(x1 + 1, int x2, int x3, num); } else { System.out.println(x1 + "+" + x2 +"+" + x3); return 1; } } this code gives me too many answe, num); } private static int solutions(int x1, num)+ solutions(x1, x2, x3[-1

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R Plot Bars are disappearing in grid.arrange/plot_grid

Post author By Full Stack
Post date January 12, 2021
No Comments on R Plot Bars are disappearing in grid.arrange/plot_grid

I have a problem with bars from my plots disappearing when I combine multiple plots. Here is the code I´m using: station<-c("Eins", "Zwei", "Drei","Vier") x1<-c(200, 185, 165,180) x2<-c(185,150,148,25) x3<-c(156,125,141,36) test.data<-data.frame(station,x1,x2,x3) plot_sof<-ggplot(test.data, aes(x=station, y=x1)) plot_sof + geom_bar(stat="identity",na.rm=TRUE) plot_sof_1<-ggplot(test.data, aes(x=station, y=x2)) plot_sof_1+geom_bar(stat = "identity",na.rm=TRUE) plot_sof_2<-ggplot(test.data, aes(x=station, y=x3)) plot_sof_2+geom_bar(stat = "identity",na.rm=TRUE) plot_grid(plot_sof, plot_sof_1, plot_sof_2, labels=c("x1", "x2", "x3")) grid.arrange(plot_sof, […]

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reverse the scale of the test outcome in the LSTM

Post author By Full Stack
Post date January 12, 2021
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I am using standardized predictors in training set to train an LSTM model. After I predict the outcome in test set, I need to reverse the predicted score back to the original scale. Normally I could just use the predicted score * SD of the trainning outcome + MEAN of the training outcome to reverse […]

Tags 'x3']) >>> x=dat.iloc[:, [1/10], [y.shape[0], 0] >>> dat.head() y x1 x2 x3 0 0.169986 0.993625 0.550893 0.119523 1 0.268806 0.860001 0.015169 0.9992, 1, 1) >>> >>> >>> means=np.mean(train_x, 1]) >>> train_x=x[0:1440] >>> train_y=y[0:1440] >>> test_x=x[1440:2880] >>> test_y=y[1440:2880] >>> >>> train_x=np.reshape(train_x, 144, 2, 3 |10, 3]] >>> y=dat.iloc[:, 4, 4) >>> >>> x=x.values # convert DataFrame to array >>> y=y.values >>> y=np.reshape(y, axis=0) >>> s_train_x=(train_x-means)/stds >>> s_test_x=(test_x-means)/stds >>> >>> model=Sequential() >>> model.add(LSTM(10, axis=0) # standardization, columns = 'y', Dense >>> from matplotlib import pyplot >>> dat=pd.DataFrame(np.random.rand(2880*4).reshape(2880, each feature in the training set were measured multiple times, However, I am using standardized predictors in training set to train an LSTM model. After I predict the outcome in test set, I need to reverse the predicted score back to the original scale. Normally I could just use the predicted score * SD of the trainning outcome, input_shape=train_X.shape[1], LSTM, multiple means and sds were returned at this step >>> stds=np.std(train_x, test_x.shape, test_x.shape[1]]) >>> test_y=np.reshape(test_y, test_y.shape) (10, test_y.shape[1]]) >>> print(train_x.shape, the standardization process returns multiple MEANs and SDs. How do I reverse the scale of the outcome to the original scale with multiple MEA, train_x.shape[1]]) >>> train_y=np.reshape(train_y, train_y.shape, train_y.shape[1]]) >>> test_x=np.reshape(test_x, when doing LSTM, x1, x2

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Set one element of each float4 in an array using CUDA/thrust

Post author By Full Stack
Post date January 11, 2021
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I have a fairly standard float4 class and an array of said float4s on the GPU. Each float4 represents an (x,y,z,rgba) point, and I’d like to use thrust to set the rgba value for each float4 in my array to a specific value. Seems like thrust::fill() might work with a custom iterator, but I don’t […]

Tags ...., "Value"), ["z1, and I'd like to use thrust to set the rgba value for each float4 in my array to a specific value. Seems like thrust::fill() might work with a, but I don't know how to write a thrust iterator. Any ideas for how to do this? (x1, c(1, c2, I have a fairly standard float4 class and an array of said float4s on the GPU. Each float4 represents an (x, rgba) point, x1, x2, y, y1, y2", z, z2"

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Simultaneously elastic net regressions

Post author By Full Stack
Post date December 22, 2020
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I’m trying to perform Elastic Net in R for multiple variables at the same time. I have 15 Xi and I want to find the elastic net model with the use of each variable as regressor. For one Xi I can perform the following and obtain optimal alpha and lambda with the following A<-matrix(rnorm(36),nrow=10,ncol = […]

Tags `[[`, "lambda.min") error <- sapply(fit$modlist, "X15") A #random data library(glmnetUtils) library(glmnet) library(coefplot) set.seed(1234) # Train model. fit <- cva.glmnet(X1 ~ ., "x3"], "x5"), {"x":"7", {"x":"9", {x:12, and a list with the coefficients that were produced with the use of the optimal alpha and min.lambda. Can someone help me do that?, b. all optimal alpha, data=A) # Get alpha. get_alpha <- function(fit) { alpha <- fit$alpha error <- sapply(fit$modlist, function(mod) {min(mod$cvm)}) alpha[which.min(error)] } # Get all parameters. get_model_params <- function(fit) { alpha <- fit$alpha l, function(mod) {min(mod$cvm)}) best <- which.min(error) data.frame(alpha = alpha[best], I'm trying to perform Elastic Net in R for multiple variables at the same time. I have 15 Xi and I want to find the elastic net model with th, lambdaMin = lambdaMin[best]) } get_model_params(fit) I want to perform this procedure simultaneously for all Xi and be able to create 2 da, ncol = 15) colnames(A) <- c("X1", nrow=10, x^4, x1':[1, x10, x13, x14, x2, x6, x8

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How to use back scale the coefficient after feature normalization in Multivariate Linear Regression

Post author By Full Stack
Post date December 15, 2020
No Comments on How to use back scale the coefficient after feature normalization in Multivariate Linear Regression

I have written below python code to find function of Multivariate Linear Regression using gradient descent. I have used feature normalization on input features and able to come up with w1,w2,w0 that minimize the loss. However, these number of w1,w2,w0 are the weights calculated after the x1, x2, and y are normalized. My goal is […]

Tags -11, -23, -32, "14", $y=w=1$, 10, 111], 136, 161, 17, 186, 2, 20, 211, 236, 26, 261, 29, 3, 4, 5, 6, 61, 7, 8, 86, 9, and hope someone could help me with this. Below are the code, and intercept(w0) to form a linear equation, and the result. import pandas as pd import numpy as np x1 = np.array([1, and y are normalized. My goal is to get simple number of the coefficient (w1, axis=0) sigma = np.std(X, axis=0) X_norm = (X-mu)/ sigma return X_norm def multivar_gradient_descent(x1, cost = {cost} ") return w1, cost = 2.553929190466498 : : Iteration 1787: w1= [1.17605931], cost = 2.659234892197519 Iteration 4: w1= [1.72977332], cost = 2.7688826449370247 Iteration 3: w1= [1.7410736], cost = 2.883051483691196 Iteration 2: w1= [1.75260451], cost = 3.0019278255843362 Iteration 1: w1= [1.76437073], cost = 3.850935442401166e-30, cost = 3.850935442401166e-30 Iteration 1789: w1= [1.17605931], cost = 3.850935442401166e-30 Iteration 1790: w1= [1.17605931], cost = 3.850935442401166e-30 Iteration 1791: w1= [1.17605931], cost = 4.560910257100077e-30 Iteration 1788: w1= [1.17605931], dtype='float64') def featureNormalize(X): """ X : The dataset of shape (m x n) """ X_norm = X.copy() mu = np.zeros(len(, dtype='float64') x2 = np.array([5, dtype='float64') y = np.array([36, I am stuck, I have written below python code to find function of Multivariate Linear Regression using gradient descent. I have used feature normalization, the result wasn't right. I think I need to back scale the coefficient to get what I want. However, then plus mean. However, these number of w1, w0 = -4.440892098500626e-19, w0 = -8.881784197001253e-19, w0 = {w0}, w0 = 0.0, w0 = 1.443289932012704e-18, w0 = multivar_gradient_descent(x1, w0 are the weights calculated after the x1, w0 by multiply it with std, w0 Result: Iteration 0: w1= [1.77637709], w0 that minimize the loss. However, w0 w1, w2, w2 = [-0.17605931], w2 = [0.37765469], w2 = [0.38895498], w2 = [0.40048588], w2 = [0.41225211], w2 = [0.42425847], w2 = {w2}, w2) of x1, which predicts y from raw input features (x1, x2, x2). I have tried converting w1, y): np.random.seed(3) w1=np.random.randn(1) # random weight initialization w2=np.random.randn(1) w0=0 iteration=2000

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Get bus error after replacing stp with str in glibc’s aarch64 memcpy.S

Post author By Full Stack
Post date December 1, 2020
No Comments on Get bus error after replacing stp with str in glibc’s aarch64 memcpy.S

For some reasons, I need to replace memcpy’s stp instruction with str, here is what I did: modified sysdeps/aarch64/memcpy.S @@ -102,11 +102,19 @@ ENTRY (MEMCPY) tbz tmp1, 5, 1f ldp B_l, B_h, [src, 16] ldp C_l, C_h, [srcend, -32] – stp B_l, B_h, [dstin, 16] – stp C_l, C_h, [dstend, -32] + //stp B_l, B_h, […]

Tags __first=) at /usr/include/c++/8/bits/stl_algobase.h:386 #3 std::__copy_move_a2<false, __first=) at /usr/include/c++/8/bits/stl_algobase.h:422 #4 std::copy<float*, __first=) at /usr/include/c++/8/bits/stl_algobase.h:455 #5 Mesh::update_single_vbo (this=0x7f54001930, __first=) at /usr/include/c++/8/bits/stl_iterator.h:783 #2 std::__copy_move_a<false, __last, -16] + //stp A_l, -16] + str C_h, -16] + str C_l, -16] + str D_h, -16] + str D_l, -16] + str F_h, -16] + str F_l, -24] + //stp F_l, -24] 1: - stp A_l, -24] ldp B_l, -32] - stp B_l, -32] - stp F_l, -32] + str C_h, -32] + str C_l, -32] + str E_h, -32] + str E_l, -32] ldp F_l, -40] + //stp B_l, -40] ldp C_l, -48] - stp B_l, -48] + str A_h, -48] + str A_l, -56] + //stp A_l, -56] + //stp G_l, -56] + sub dstend, -64] - stp A_l, -64] - stp G_l, -64] + str E_h, -64] + str E_l, -64]! @@ -234, -8] ldp A_l, -8] ret .p2align 4 @@ -150, -8] ret .p2align 4 @@ -224, -8] ret /* Align DST to 16 byte alignment so that we don't cross cache line @@ -171, :Float, 'src'), "DST"), "x3"], "x5"), [dstend, [dstin, [dstin] - stp B_l, [dstin] - stp D_l, [dstin] [Current thread is 1 (Thread 0x7f80e77a70 (LWP 9281))] (gdb) info registers x0 0x7f782368ac 547476433068 x1, [dstin] /* Oops, [dstin] + str A_h, [dstin] + str A_l, [dstin] + str C_h, [dstin] + str C_l, [dstin] + str D_h, [dstin] + str D_l, [src] - stp D_l, [src] sub src, [srcend, [x0] 0x0000007f9272d31c : str x7, [x0] 0x0000007f9272d344 : stur x7, [x0] 0x0000007f9272d360 : stur w7, [x0] 0x0000007f9272d380 : strb w8, [x0] 0x0000007f9272d3a8 : str x7, [x0] 0x0000007f9272d3fc : str x13, [x0] 0x0000007f9272d57c : str x11, [x1] 0x0000007f9272d2d4 : add x4, [x1] 0x0000007f9272d2f4 : tbnz w14, [x1] 0x0000007f9272d33c : ldur x7, [x1] 0x0000007f9272d358 : ldur w7, [x1] 0x0000007f9272d374 : ldurb w7, [x1] 0x0000007f9272d3ec : sub x1, [x1] 0x0000007f9272d558 : stur x12, {"x":"7", {"x":"9", #-1] 0x0000007f9272d378 : ldrb w8, #-1] 0x0000007f9272d388 : ret 0x0000007f9272d38c : nop 0x0000007f9272d390 : ldp x8, #-16] 0x0000007f9272d2fc : tbz w14, #-16] 0x0000007f9272d324 : stur x13, #-16] 0x0000007f9272d3a4 : str x6, #-16] 0x0000007f9272d3d0 : stur x3, #-16] 0x0000007f9272d478 : str x12, #-16] 0x0000007f9272d49c : stur x11, #-16] 0x0000007f9272d4c4 : sub x4, #-16] 0x0000007f9272d4d0 : stur x12, #-16] 0x0000007f9272d4d4 : stur x13, #-16] 0x0000007f9272d4f8 : stur x7, #-16] 0x0000007f9272d500 : stur x8, #-16] 0x0000007f9272d538 : stur x7, #-24] 0x0000007f9272d318 : str x6, #-24] 0x0000007f9272d3cc : stur x4, #-24] 0x0000007f9272d498 : stur x10, #-24] 0x0000007f9272d508 : ldp x8, #-24] 0x0000007f9272d548 : ldp x8, #-32] 0x0000007f9272d308 : str x8, #-32] 0x0000007f9272d314 : stur x11, #-32] 0x0000007f9272d3a0 : ldp x4, #-32] 0x0000007f9272d3c8 : stur x2, #-32] 0x0000007f9272d46c : str x10, #-32] 0x0000007f9272d494 : stur x9, #-32] 0x0000007f9272d4dc : ldp x10, #-32] 0x0000007f9272d504 : stur x9, #-32] 0x0000007f9272d50c : stur x10, #-32] 0x0000007f9272d544 : stur x9, #-4] 0x0000007f9272d35c : str w6, #-4] 0x0000007f9272d364 : ret 0x0000007f9272d368 : cbz x2, #-40] 0x0000007f9272d490 : stur x8, #-40] 0x0000007f9272d514 : ldp x10, #-40] 0x0000007f9272d554 : ldp x10, #-48] 0x0000007f9272d460 : str x8, #-48] 0x0000007f9272d48c : stur x7, #-48] 0x0000007f9272d4e0 : ldp x12, #-48] 0x0000007f9272d510 : stur x11, #-48] 0x0000007f9272d518 : stur x12, #-48] 0x0000007f9272d550 : stur x11, #-56] 0x0000007f9272d488 : stur x6, #-56] 0x0000007f9272d520 : sub x5, #-56] 0x0000007f9272d560 : str x2, #-64] --Type for more, #-64] 0x0000007f9272d454 : str x6, #-64] 0x0000007f9272d484 : stur x2, #-64] 0x0000007f9272d51c : stur x13, #-64]! 0x0000007f9272d4e4 : sub x5, #-64]! 0x0000007f9272d528 : subs x2, #-8] 0x0000007f9272d328 : ret 0x0000007f9272d32c : nop 0x0000007f9272d330 : cmp x2, #-8] 0x0000007f9272d340 : str x6, #-8] 0x0000007f9272d348 : ret 0x0000007f9272d34c : nop 0x0000007f9272d350 : tbz w2, #-8] 0x0000007f9272d3d4 : ret 0x0000007f9272d3d8 : nop 0x0000007f9272d3dc : nop 0x0000007f9272d3e0, #-8] 0x0000007f9272d4a0 : ret 0x0000007f9272d4a4 : nop 0x0000007f9272d4a8 : nop 0x0000007f9272d4ac, #-8] 0x0000007f9272d4d8 : ldp x8, #-8] 0x0000007f9272d4fc : ldp x6, #-8] 0x0000007f9272d53c : ldp x6, #0x1 0x0000007f9272d2f0 : ldp x6, #0x10 0x0000007f9272d2e0 : b.ls 0x7f9272d330 // b.plast 0x0000007f9272d2e4 : cmp x2, #0x40 0x0000007f9272d444 : ldp x12, #0x40 0x0000007f9272d44c : b.hi 0x7f9272d414 // b.pmore 0x0000007f9272d450 : ldp x1, #0x40 0x0000007f9272d524 : ldp x12, #0x40 0x0000007f9272d52c : b.hi 0x7f9272d4f4 // b.pmore 0x0000007f9272d530 : ldp x2, #0x60 0x0000007f9272d2e8 : b.hi 0x7f9272d3e0 // b.pmore 0x0000007f9272d2ec : sub x14, #0x8 0x0000007f9272d334 : b.cc 0x7f9272d350 // b.lo, #0x80 0x0000007f9272d4ec : b.ls 0x7f9272d530 // b.plast 0x0000007f9272d4f0 : nop 0x000000, #0x90 0x0000007f9272d410 : b.ls 0x7f9272d450 // b.plast --Type for more, #0xf 0x0000007f9272d3e4 : and x3, #0xf 0x0000007f9272d4c0 : ldp x12, #0xfffffffffffffff0 0x0000007f9272d3e8 : ldp x12, #1 --Type for more, #16] => 0x0000007f9272d3f8 : str x12, #16] 0x0000007f9272d304 : ldp x10, #16] 0x0000007f9272d30c : str x9, #16] 0x0000007f9272d394 : ldp x10, #16] 0x0000007f9272d3b0 : str x9, #16] 0x0000007f9272d418 : str x7, #16] 0x0000007f9272d420 : str x8, #16] 0x0000007f9272d458 : str x7, #16] 0x0000007f9272d54c : stur x10, #16] 0x0000007f9272d574 : str x9, #24] 0x0000007f9272d310 : stur x10, #24] 0x0000007f9272d3b4 : str x10, #24] 0x0000007f9272d41c : ldp x6, #24] 0x0000007f9272d45c : ldp x6, #24] 0x0000007f9272d578 : str x10, #32] 0x0000007f9272d398 : ldp x12, #32] 0x0000007f9272d3b8 : str x11, #32] 0x0000007f9272d404 : ldp x10, #32] 0x0000007f9272d424 : str x9, #32] 0x0000007f9272d42c : str x10, #32] 0x0000007f9272d464 : str x9, #32] 0x0000007f9272d540 : stur x8, #32] 0x0000007f9272d56c : str x7, #40] 0x0000007f9272d3bc : str x12, #40] 0x0000007f9272d428 : ldp x8, #40] 0x0000007f9272d468 : ldp x8, #40] 0x0000007f9272d570 : str x8, #48] 0x0000007f9272d39c : ldp x1, #48] 0x0000007f9272d3c0 : str x13, #48] 0x0000007f9272d408 : ldp x12, #48] 0x0000007f9272d430 : str x11, #48] 0x0000007f9272d438 : str x12, #48] 0x0000007f9272d470 : str x11, #48] 0x0000007f9272d534 : stur x6, #48] 0x0000007f9272d564 : str x3, #56] 0x0000007f9272d3c4 : stur x1, #56] 0x0000007f9272d434 : ldp x10, #56] 0x0000007f9272d474 : ldp x10, #56] 0x0000007f9272d568 : str x6, #64] 0x0000007f9272d43c : str x13, #64] 0x0000007f9272d47c : str x13, #64]! 0x0000007f9272d40c : subs x2, #64]! 0x0000007f9272d448 : subs x2, #72] 0x0000007f9272d440 : add x3, #72] 0x0000007f9272d480 : stur x1, #8] 0x0000007f9272d320 : stur x12, #8] 0x0000007f9272d3ac : str x8, #8] 0x0000007f9272d400 : ldp x8, #8] 0x0000007f9272d580 : ret End of assembler dump., 0x7f9272d318 0x0000007f9272d300 : ldp x8, 0x7f9272d368 0x0000007f9272d354 : ldr w6, 0x7f9272d388 0x0000007f9272d36c : lsr x14, 0x7f9272d390 0x0000007f9272d2f8 : ldp x12, 0x7f9272d580 0x0000007f9272d4b4 : add x4, 11 +102, 12 +158, 128 + 16 /* Test and readjust count. */ b.ls L(last64) L(loop64): - stp A_l, 13 +283, 15 bic dst, 15 ldp D_l, 16] - stp B_l, 16] - stp C_l, 16] - stp D_l, 16] + str A_h, 16] + str A_l, 16] + str B_h, 16] + str B_l, 16] ldp C_l, 17 +225, 17 +308, 19 @@ ENTRY (MEMCPY) tbz tmp1, 1f ldp B_l, 2, 20 +191, 22 @@ L(move_long): nop 1: - stp A_l, 24 @@ L(copy96): ldp D_l, 24] + //stp C_l, 24] ldp A_l, 31 @@ L(copy_long): sub src, 32] - stp C_l, 32] - stp D_l, 32] + str A_h, 32] + str A_l, 32] + str B_h, 32] + str B_l, 32] ldp C_l, 33 @@ L(loop64): there is just 1 byte left. */ L(last64): ldp E_l, 33 @@ L(move_long): there is just 1 byte left. */ 2: ldp G_l, 40] + //stp D_l, 40] ldp B_l, 48] - stp A_l, 48] - stp D_l, 48] - stp E_l, 48] + //stp C_l, 48] + str C_h, 48] + str C_l, 48] + str D_h, 48] + str D_l, 48] + str G_h, 48] + str G_l, 48] ldp D_l, 48] ldp E_l, 5, 56] + //stp E_l, 56] ldp C_l, 6, 64 b.hi 1b @@ -250, 64 b.hi L(loop64) @@ -194, 64 ldp D_l, 64] - stp E_l, 64] + str D_h, 64]! + //stp D_l, 64]! + str D_l, 64]! subs count, 7 +271, 72] + //stp E_l, 72] + add dst, 8] + //stp B_l, 8] + //stp D_l, 8] 3: ret END (MEMCPY) (if you want to view diff in two windows, 8] ldp B_l, 8] Question: Why do I get bus error here? How to fix it? update here is disassembler of memcpy: (gdb) disassemble __memcpy_generic Dump, 9 @@ L(move_long): sub srcend, A_h, B_h, b.last 0x0000007f9272d338 : ldr x6, b.ul, bus error! */ str D_h, Bus error. #0 __memcpy_generic () at ../sysdeps/aarch64/multiarch/../memcpy.S:195 warning: Source file is more recent than executable. 195, c to continue without paging-- 0x0000007f9272d370 : ldrb w6, c to continue without paging-- 0x0000007f9272d414 : str x6, c to continue without paging-- 0x0000007f9272d4b8 : add x5, c to continue without paging-- 0x0000007f9272d55c : stur x13, c to continue without paging-- x23 0x7f7820c000 547476258816 x24 0x4d4000 5062656 x25 0x11, C_h, capacity 16 = {...}, capacity 16 = {...}) at /usr/include/c++/8/bits/stl_vector.h:930 #7 0x000000000041e094 in WaveMesh::update (elapsed=0.060067000000003645, count) -> ((), D_h, e_h, F_h, float*> (__result=, for some reasons, G_h, here is what I did: modified sysdeps/aarch64/memcpy.S @@ -102, I need to replace memcpy's stp instruction with str, it generates a bus error: Using host libthread_db library "/lib/aarch64-linux-gnu/libthread_db.so.1". Core was generated by `./build/src/glm, it triggered bus error in the first str here: L(copy_long): and tmp1, n, nfloats=) at ../src/mesh.cpp:469 #6 0x0000000000478308 in Mesh::update_vbo (this=0x7f54001930, pls visit https://www.diffchecker.com/qAgmBLFu) glibc compiled successfully and reboot is also ok. But when I run glmark2, q to quit, ranges=std::vector of length 10, std::random_access_iterator_tag>::__copy_m (__result=, this=0x7f54001930) at ../src/scene-buffer.cpp:163 #8 SceneBuffer::update (this=) at ../src/scene-buffer.cpp:434 #9 0x0000000, tmp1 /* Count is now 16 too large. */ ldp A_l, tmp1 add count, tmp1 ldp A_l, tmp1 sub count, true, x^4, x0, x1, x1':[1, x13, x14 0x0000007f9272d3f0 : add x2, x14 0x0000007f9272d3f4 : ldp x6, x14 0x0000007f9272d4c8 : sub x2, x14 0x0000007f9272d4cc : ldp x6, x14 0x0000007f9272d4e8 : subs x2, x14] 0x0000007f9272d37c : strb w6, x14] 0x0000007f9272d384 : sturb w7, x2, x2 --Type for more, x2 0x0000007f9272d2d8 : add x5, x2 0x0000007f9272d2dc : cmp x2, x2 0x0000007f9272d4bc : and x14

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Using shortest path pgr_dijkstra

Post author By Technical Analysis
Post date November 29, 2020
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Versions PostGIS Bundle 3 for PostgreSQL x64 12 pgAdmin 4.24 pgrouting 3.1.0 I have also enabled the postgis and pgrouting extensions on a public schema. I am only just starting with pgrouting so I am new to all the concepts. I am trying to follow a tutorial from pgrouting.org to find the shortest path between […]

Tags -2] [3, 'target', $cost ); }, 0, 0.001); Then I try to use the pgr_dijkstra() function and it's at this point the wheels come off. The textbook I'm following gives the foll, 0.5, 1, 1.999999999999, 100, 130, 16, 2, 3, 3.5), 4, 4); At this point I have created the geometry using: UPDATE edge_table SET the_geom = st_makeline(st_point(x1, 50, 80, 9, boolean, boolean) does not exist LINE 1: SELECT pgr_dijkstra('SELECT id, capacity, capacity BIGINT, category_id INTEGER, cost FLOAT, cost FROM ed... ^ HINT: No function matches the given name and argument types. You might need to add explicit type casts. SQL, cost FROM edge_table', dir character varying, false, false); But it throws this error. ERROR: function pgr_dijkstra(unknown, Integer> { }, reverse_capacity, reverse_capacity BIGINT, reverse_category_id, reverse_category_id INTEGER, reverse_cost, reverse_cost FLOAT, source, source BIGINT, st_point(x2, target BIGINT, the_geom geometry INSERT INTO edge_table ( category_id, Versions PostGIS Bundle 3 for PostgreSQL x64 12 pgAdmin 4.24 pgrouting 3.1.0 I have also enabled the postgis and pgrouting extensions on a pu, x1, x1 FLOAT, x2, x2 FLOAT, y1, y1 FLOAT, y2 FLOAT, y2) VALUES (3, y2)) After that I create some topology with : SELECT pgr_createTopology('edge_table'

Mastering Development

convert List to data table in R with Binary entries (0 or 1)

Post author By Full Stack
Post date November 26, 2020
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I have a list as below: list("X1", c("X1", "X2"), "X3") I want to transform this list to data table as follows: The columns in data table will be as per all the unique entries in the list. In this example, the data frame will have columns – X1, X2, X3 The data table will have […]

Tags -3L)) As my list contains more than 80, "X3") I want to transform this list to data table as follows: The columns in data table will be as per all the unique entries in the list., 0, 000 elements, 1, class = "data.frame", cx1, I am looking for a solution that uses data table. Thanks, I have a list as below: list("X1", row.names = c(NA, the data frame will have columns - X1, the row 1 will have column X1 as 1 while other columns (X2 and X3) as 0 c) The row 2 will have columns X1 and X2 as 1 while X3 as 0 and so on, x2, x2 = c(0, X3 = c(0, X3 The data table will have 3 rows as equivalent to length of list Filling values in data table a) The data table will have binary values (

Mastering Development

Problem with Standart Deviation calculation

Post author By Full Stack
Post date November 23, 2020
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I have this code where two evaluations about an employee are inserted by the user (organized in struct), I need to calculate the standard deviation and the variance between them. But it’s not working. What should I do? typedef struct info { char name[30]; float x1, x2; float aveg; float sum; }emp; float resultVar(emp aveg, […]

Tags &n); for (i = 0; i < n; i++){ printf("employee: "); scanf("%s %f %f", &p[i].name, &p[i].x1, &p[i].x2); for (j = 0; j < 2; j++){ p[i].aveg= p[i].x1 + p[i].x2; } p[i].aveg= p[i].aveg/ 2.0; }, a, emp p[], i, I have this code where two evaluations about an employee are inserted by the user (organized in struct), I need to calculate the standard deviation and the variance between them. But it's not working. What should I do? typedef struct info {, int n){ int i; float std; for (i = 0; i < n; i++) { std= std+ (p[i].sum- p[i].aveg) * (p[i].sum- p[i].aveg); }, int n){ int i; float var= 0.0, j; printf("number of employeers:"); scanf("%d", n); printf("v: %f \nsd: %f", n); std= resultStd(var, std); return 0; }, std= 0.0, std= 0.0; for (i = 0; i < n; i++) { var= var+ p[i].aveg; } var = var/ n; return var; } float resultStd(float var, sum; float x1, var(), var= 0.0; char name[30]; int n, x2, x2; float aveg; float sum; }emp; float resultVar(emp aveg