optimization

问题 This question already has answers here : How to print number with commas as thousands separators? (27 answers) Closed 7 years ago . I don't really know the "name" for this problem, so it might be a incorrect title, but the problem is simple, if I have a number for example: number = 23543 second = 68471243 I want to it make print() like this. 23,543 68,471,243 I hope this explains enough or else add comments. Any help is appreciated! 回答1: If you only need to add comma as thousand separator and

问题 I am using scipy optimize to get the minimum value on the following function: def randomForest_b(a,b,c,d,e): return abs(rf_diff.predict([[a,b,c,d,e]])) I eventually want to be able to get the optimal values of (a) and (b) given the arguments (c,d,e). However, just to learn how to work the optimize function, I am trying to get the optimal value of (a) given the other arguments. I have the following code: res=optimize.minimize(randomForest_b, x0=45,args=(119.908500,65.517527,2.766103,29.509200)

问题 I am using scipy optimize to get the minimum value on the following function: def randomForest_b(a,b,c,d,e): return abs(rf_diff.predict([[a,b,c,d,e]])) I eventually want to be able to get the optimal values of (a) and (b) given the arguments (c,d,e). However, just to learn how to work the optimize function, I am trying to get the optimal value of (a) given the other arguments. I have the following code: res=optimize.minimize(randomForest_b, x0=45,args=(119.908500,65.517527,2.766103,29.509200)

问题 I am looking for a more efficient way of doing an operation with a given dataframe. library(purrr) library(dplyr) Here is a step by step description: First, there is the function possible_matches , that for each observation i in df , gives the index of rows that are possibly matchable to i , which are going to be used on the next step: possible_matches <- function(i, df) { k1 <- df$j[df$id_0 == df$id_0[i]] j2 <- setdiff(df$j, k1) k2 <- map(j2, ~ df$j[df$id_0[.] == df$id_0]) k3 <- map(k2, ~

问题 I am looking for a more efficient way of doing an operation with a given dataframe. library(purrr) library(dplyr) Here is a step by step description: First, there is the function possible_matches , that for each observation i in df , gives the index of rows that are possibly matchable to i , which are going to be used on the next step: possible_matches <- function(i, df) { k1 <- df$j[df$id_0 == df$id_0[i]] j2 <- setdiff(df$j, k1) k2 <- map(j2, ~ df$j[df$id_0[.] == df$id_0]) k3 <- map(k2, ~

问题 I am looking for a more efficient way of doing an operation with a given dataframe. library(purrr) library(dplyr) Here is a step by step description: First, there is the function possible_matches , that for each observation i in df , gives the index of rows that are possibly matchable to i , which are going to be used on the next step: possible_matches <- function(i, df) { k1 <- df$j[df$id_0 == df$id_0[i]] j2 <- setdiff(df$j, k1) k2 <- map(j2, ~ df$j[df$id_0[.] == df$id_0]) k3 <- map(k2, ~

问题 I am looking for a more efficient way of doing an operation with a given dataframe. library(purrr) library(dplyr) Here is a step by step description: First, there is the function possible_matches , that for each observation i in df , gives the index of rows that are possibly matchable to i , which are going to be used on the next step: possible_matches <- function(i, df) { k1 <- df$j[df$id_0 == df$id_0[i]] j2 <- setdiff(df$j, k1) k2 <- map(j2, ~ df$j[df$id_0[.] == df$id_0]) k3 <- map(k2, ~

问题 On x86-64 platforms, the CLFLUSH assembly instruction allows to flush the cache line corresponding to a given address. Instead of flushing the cache related to a specific address, would there be a way to flush the entire cache (either the cache related to the program being executed, or the entire cache), for example by making it full of dummy contents (or any other approach I would not be aware of): using only standard C++17? using standard C++17 and compiler intrinsics if necessary? What

问题 On x86-64 platforms, the CLFLUSH assembly instruction allows to flush the cache line corresponding to a given address. Instead of flushing the cache related to a specific address, would there be a way to flush the entire cache (either the cache related to the program being executed, or the entire cache), for example by making it full of dummy contents (or any other approach I would not be aware of): using only standard C++17? using standard C++17 and compiler intrinsics if necessary? What

问题 If the compiler can prove that a (private) member of a class is never used, including by potential friends, does the standard allow the compiler to remove this member from the memory footprint of the class? It is self-evident that this not possible for protected or public members at compile time, but there could be circumstances where it is possible regarding private data members for such a proof to be constructed. Related questions: Behind the scenes of public, private and protected (sparked