A Rust vector, also known as a “vec”, refers to a dynamic array that can grow or shrink as needed. It is a standard library type and is one of Rust’s most commonly used data structures. Internally, a vec is represented by a heap-allocated buffer that holds a sequence of elements ...
这应该算是Rust语言在设计上的一个亮点。 当然,我们也可以通过动态分发来处理错误类型,但这是一把双刃剑,谨慎使用。 fnget_file_names(dir_path:&Path)->std::result::Result<Vec<String>,Box<dynstd::error::Error>>{...} 上面的代码来源于我目前正在开发的笔记系统,都能编译通过,但我接触Rust的时间也不...
#include<iostream>#include<iterator>#include<string>#include<vector>using std::cin;using std::cout;using std::endl;using std::string;using std::vector;intmain(){string tmp_string="This will be converted to char*\n";vector<char>vec_str(tmp_string.begin(),tmp_string.end());std::copy(...
("zero_tensor: {:?}",zero_tensor.to_vec1::()?);letones_tensor = tensor.ones_like()?;println!("ones_tensor: {:?}",ones_tensor.to_vec1::()?);letrandom_tensor = tensor.rand_like(0.0,1.0)?;println!("random_tensor: {:?}",random_tensor.to_vec1::<f64>()?); Checking tensor...
I have a medium sized project using WGPU and if one single function is added rustc fails to link If this function is commented out, everything works just fine. The code of the function that causes the problem: pub fn update(&self, queue:...
usearrow2::array::{Array,MutableArray};usearrow2_convert::serialize::arrow_serialize_to_mutable_array;usearrow2_convert::deserialize::arrow_array_deserialize_iterator_as_type;usearrow2_convert::field::I128;usestd::borrow::Borrow;fnconvert_i128(){letoriginal_array =vec![1asi128,2,3];letb:...
Convert it to Lance import lance from lance.vector import vec_to_table import numpy as np import struct nvecs = 1000000 ndims = 128 with open("sift/sift_base.fvecs", mode="rb") as fobj: buf = fobj.read() data = np.array(struct.unpack("<128000000f", buf[4 : 4 + 4 * nvecs...
Convert it to Lance import lance from lance.vector import vec_to_table import numpy as np import struct nvecs = 1000000 ndims = 128 with open("sift/sift_base.fvecs", mode="rb") as fobj: buf = fobj.read() data = np.array(struct.unpack("<128000000f", buf[4 : 4 + 4 * nvecs...
Candle: in candle, the data type needs to be explicitly specified; usecandle_core::{DType,Device,Tensor};useanyhow::Result;letdata:[u32;3]=[1u32,2,3];lettensor =Tensor::new(&data,&Device::Cpu)?;println!("tensor: {:?}",tensor.to_vec1::()?);letnested_data:[[u32;3];3]=[[...