与其他语言的显著差异

与 MATLAB 的显著差异

虽然 MATLAB 用户可能会发现 Julia 的语法很熟悉，但 Julia 不是 MATLAB 的克隆。 它们之间存在重大的语法和功能差异。 以下是一些可能会使习惯于 MATLAB 的Julia用户感到困扰的显著差异：

  • Julia 数组使用方括号 A[i,j] 进行索引。
  • Julia 的数组在赋值给另一个变量时不发生复制。执行 A = B 后，改变 B 中元素也会修改 A。 as well.
  • Julia 的值在向函数传递时不发生复制。如果某个函数修改了数组，这一修改 对调用者是可见的。
  • Julia 不会在赋值语句中自动增长数组。 而在 MATLAB 中 a(4) = 3.2 可以创建数组 a = [0 0 0 3.2] 和 a(5) = 7 可以将它增长为 a = [0 0 0 3.2 7]。 如果 a 的长度小于 5 或者这个语句是第一次使用标识符 a，则相应的 Julia 语句 a[5] = 7 会抛出错误。 Julia 使用 push! 和 append! 实现比 MATLAB 的 a(end+1) = val 更高效地增长 Vector。 which grow Vectors much more efficiently than MATLAB's a(end+1) = val.
  • 虚数单位 sqrt(-1) 在Julia中表示为 im，而不是在 MATLAB 中的 i 或 j。
  • 在 Julia 中，没有小数点的字面数字（例如 42）会创建整数而不是浮点数。 也支持任意大整数。 因此，某些操作（如 2^-1）将抛出domain error，因为结果不是整数（有关详细信息，请参阅 the FAQ entry on domain errors）。 for details).
  • 在 Julia 中，能返回多个值并将其赋值为元组，例如 (a, b) = (1, 2) 或 a, b = 1, 2。 在 Julia 中不存在 MATLAB 的 nargout，它通常在 MATLAB 中用于根据返回值的数量执行可选工作。 取而代之的是，用户可以使用可选参数（optional arguments）和关键字参数（keyword arguments）来实现类似的功能。 similar capabilities.
  • Julia 拥有真正的一维数组。 列向量的大小为 N，而不是 Nx1。 例如，rand(N) 创建一个一维数组。
  • 在 Julia 中，[x,y,z] 将始终构造一个包含x，y 和 z 的3元素数组。
    • 要在第一个维度（“垂直列”）中连接元素，请使用 vcat(x,y,z) 或用分号分隔（[x; y; z]）。 with semicolons ([x; y; z]).
    • 要在第二个维度（“水平行”）中连接元素，请使用 hcat(x,y,z) 或用空格分隔（[x y z]）。 with spaces ([x y z]).
    • 要构造块矩阵（在前两个维度中连接元素），请使用 hvcat或组合空格和分号（[a b; c d]）。 or combine spaces and semicolons ([a b; c d]).
  • 在 Julia 中，a:b 和 a:b:c 构造 AbstractRange 对象。使用 collect(a:b) 构造一个类似 MATLAB 中完整的向量。 通常，不需要调用 collect。 在大多数情况下，AbstractRange 对象将像普通数组一样运行，但效率更高，因为它是懒惰求值。 这种创建专用对象而不是完整数组的模式经常被使用，并且 也可以在诸如 range 之类的函数中看到，或者在诸如 enumerate 和 zip 之类的迭代器中看到。 特殊对象大多可以像正常数组一样使用。
  • Julia 中的函数返回其最后一个表达式或 return 关键字的值而无需 在函数定义中列出要返回的变量的名称（有关详细信息，请参阅 @ref">The return Keyword）。 for details).
  • Julia 脚本可以包含任意数量的函数，并且在加载文件时，所有定义都将在外部可见。 可以从当前工作目录之外的文件加载函数定义。 directory.
  • In Julia, reductions such as sum, prod, and max are performed over every element of an array when called with a single argument, as in sum(A), even if A has more than one dimension.
  • In Julia, parentheses must be used to call a function with zero arguments, like in rand().
  • Julia discourages the used of semicolons to end statements. The results of statements are not automatically printed (except at the interactive prompt), and lines of code do not need to end with semicolons. println or @printf"">@printf can be used to print specific output.
  • In Julia, if A and B are arrays, logical comparison operations like A == B do not return an array of booleans. Instead, use A .== B, and similarly for the other boolean operators like <, > and =.
  • In Julia, the operators &, |, and ⊻ (xor) perform the bitwise operations equivalent to and, or, and xor respectively in MATLAB, and have precedence similar to Python's bitwise operators (unlike C). They can operate on scalars or element-wise across arrays and can be used to combine logical arrays, but note the difference in order of operations: parentheses may be required (e.g., to select elements of A equal to 1 or 2 use (A .== 1) .| (A .== 2)).
  • In Julia, the elements of a collection can be passed as arguments to a function using the splat operator …, as in xs=[1,2]; f(xs…).
  • Julia's svd returns singular values as a vector instead of as a dense diagonal matrix.
  • In Julia, … is not used to continue lines of code. Instead, incomplete expressions automatically continue onto the next line.
  • In both Julia and MATLAB, the variable ans is set to the value of the last expression issued in an interactive session. In Julia, unlike MATLAB, ans is not set when Julia code is run in non-interactive mode.
  • Julia's structs do not support dynamically adding fields at runtime, unlike MATLAB's classes. Instead, use a Dict.
  • In Julia each module has its own global scope/namespace, whereas in MATLAB there is just one global scope.
  • In MATLAB, an idiomatic way to remove unwanted values is to use logical indexing, like in the expression x(x>3) or in the statement x(x>3) = [] to modify x in-place. In contrast, Julia provides the higher order functions filter and filter!, allowing users to write filter(z->z>3, x) and filter!(z->z>3, x) as alternatives to the corresponding transliterations x[x.>3] and x = x[x.>3]. Using filter! reduces the use of temporary arrays.
  • The analogue of extracting (or "dereferencing") all elements of a cell array, e.g. in vertcat(A{:}) in MATLAB, is written using the splat operator in Julia, e.g. as vcat(A…).

    与 R 的显著差异

One of Julia's goals is to provide an effective language for data analysis and statistical programming. For users coming to Julia from R, these are some noteworthy differences:

  • Julia's single quotes enclose characters, not strings.

  • Julia can create substrings by indexing into strings. In R, strings must be converted into character vectors before creating substrings.

  • In Julia, like Python but unlike R, strings can be created with triple quotes """ … """. This syntax is convenient for constructing strings that contain line breaks.

  • In Julia, varargs are specified using the splat operator …, which always follows the name of a specific variable, unlike R, for which … can occur in isolation.

  • In Julia, modulus is mod(a, b), not a %% b. % in Julia is the remainder operator.

  • In Julia, not all data structures support logical indexing. Furthermore, logical indexing in Julia is supported only with vectors of length equal to the object being indexed. For example:

    • In R, c(1, 2, 3, 4)[c(TRUE, FALSE)] is equivalent to c(1, 3).
    • In R, c(1, 2, 3, 4)[c(TRUE, FALSE, TRUE, FALSE)] is equivalent to c(1, 3).
    • In Julia, [1, 2, 3, 4][[true, false]] throws a BoundsError.
    • In Julia, [1, 2, 3, 4][[true, false, true, false]] produces [1, 3].

  • Like many languages, Julia does not always allow operations on vectors of different lengths, unlike R where the vectors only need to share a common index range. For example, c(1, 2, 3, 4) + c(1, 2) is valid R but the equivalent [1, 2, 3, 4] + [1, 2] will throw an error in Julia.

  • Julia allows an optional trailing comma when that comma does not change the meaning of code. This can cause confusion among R users when indexing into arrays. For example, x[1,] in R would return the first row of a matrix; in Julia, however, the comma is ignored, so x[1,] == x[1], and will return the first element. To extract a row, be sure to use :, as in x[1,:].

  • Julia's map takes the function first, then its arguments, unlike lapply(, function, …) in R. Similarly Julia's equivalent of apply(X, MARGIN, FUN, …) in R is mapslices where the function is the first argument.

  • Multivariate apply in R, e.g. mapply(choose, 11:13, 1:3), can be written as broadcast(binomial, 11:13, 1:3) in Julia. Equivalently Julia offers a shorter dot syntax for vectorizing functions binomial.(11:13, 1:3).

  • Julia uses end to denote the end of conditional blocks, like if, loop blocks, like while/ for, and functions. In lieu of the one-line if ( cond ) statement, Julia allows statements of the form if cond; statement; end, cond && statement and !cond || statement. Assignment statements in the latter two syntaxes must be explicitly wrapped in parentheses, e.g. cond && (x = value).

  • In Julia, <-, <<- and -> are not assignment operators.

  • Julia's -> creates an anonymous function.

  • Julia constructs vectors using brackets. Julia's [1, 2, 3] is the equivalent of R's c(1, 2, 3).

  • Julia's * operator can perform matrix multiplication, unlike in R. If A and B are matrices, then A B denotes a matrix multiplication in Julia, equivalent to R's A %% B. In R, this same notation would perform an element-wise (Hadamard) product. To get the element-wise multiplication operation, you need to write A .* B in Julia.

  • Julia performs matrix transposition using the transpose function and conjugated transposition using the ' operator or the adjoint function. Julia's transpose(A) is therefore equivalent to R's t(A). Additionally a non-recursive transpose in Julia is provided by the permutedims function.

  • Julia does not require parentheses when writing if statements or for/while loops: use for i in [1, 2, 3] instead of for (i in c(1, 2, 3)) and if i == 1 instead of if (i == 1).

  • Julia does not treat the numbers 0 and 1 as Booleans. You cannot write if (1) in Julia, because if statements accept only booleans. Instead, you can write if true, if Bool(1), or if 1==1.

  • Julia does not provide nrow and ncol. Instead, use size(M, 1) for nrow(M) and size(M, 2) for ncol(M).

  • Julia is careful to distinguish scalars, vectors and matrices. In R, 1 and c(1) are the same. In Julia, they cannot be used interchangeably.

  • Julia's diag and diagm are not like R's.

  • Julia cannot assign to the results of function calls on the left hand side of an assignment operation: you cannot write diag(M) = fill(1, n).

  • Julia discourages populating the main namespace with functions. Most statistical functionality for Julia is found in packages under the JuliaStats organization. 例如：

  • Julia provides tuples and real hash tables, but not R-style lists. When returning multiple items, you should typically use a tuple or a named tuple: instead of list(a = 1, b = 2), use (1, 2) or (a=1, b=2).

  • Julia encourages users to write their own types, which are easier to use than S3 or S4 objects in R. Julia's multiple dispatch system means that table(x::TypeA) and table(x::TypeB) act like R's table.TypeA(x) and table.TypeB(x).

  • In Julia, values are not copied when assigned or passed to a function. If a function modifies an array, the changes will be visible in the caller. This is very different from R and allows new functions to operate on large data structures much more efficiently.

  • In Julia, vectors and matrices are concatenated using hcat, vcat and hvcat, not c, rbind and cbind like in R.

  • In Julia, a range like a:b is not shorthand for a vector like in R, but is a specialized AbstractRange object that is used for iteration without high memory overhead. To convert a range into a vector, use collect(a:b).

  • Julia's max and min are the equivalent of pmax and pmin respectively in R, but both arguments need to have the same dimensions. While maximum and minimum replace max and min in R, there are important differences.

  • Julia's sum, prod, maximum, and minimum are different from their counterparts in R. They all accept one or two arguments. The first argument is an iterable collection such as an array. If there is a second argument, then this argument indicates the dimensions, over which the operation is carried out. For instance, let A = [1 2; 3 4] in Julia and B <- rbind(c(1,2),c(3,4)) be the same matrix in R. Then sum(A) gives the same result as sum(B), but sum(A, dims=1) is a row vector containing the sum over each column and sum(A, dims=2) is a column vector containing the sum over each row. This contrasts to the behavior of R, where separate colSums(B) and rowSums(B) functions provide these functionalities. If the dims keyword argument is a vector, then it specifies all the dimensions over which the sum is performed, while retaining the dimensions of the summed array, e.g. sum(A, dims=(1,2)) == hcat(10). It should be noted that there is no error checking regarding the second argument.

  • Julia has several functions that can mutate their arguments. For example, it has both sort and sort!.

  • In R, performance requires vectorization. In Julia, almost the opposite is true: the best performing code is often achieved by using devectorized loops.

  • Julia is eagerly evaluated and does not support R-style lazy evaluation. For most users, this means that there are very few unquoted expressions or column names.

  • Julia does not support the NULL type. The closest equivalent is nothing, but it behaves like a scalar value rather than like a list. Use x == nothing instead of is.null(x).

  • In Julia, missing values are represented by the missing object rather than by NA. Use ismissing(x) instead of isna(x). The skipmissing function is generally used instead of na.rm=TRUE (though in some particular cases functions take a skipmissing argument).

  • Julia lacks the equivalent of R's assign or get.

  • In Julia, return does not require parentheses.

  • In R, an idiomatic way to remove unwanted values is to use logical indexing, like in the expression x[x>3] or in the statement x = x[x>3] to modify x in-place. In contrast, Julia provides the higher order functions filter and filter!, allowing users to write filter(z->z>3, x) and filter!(z->z>3, x) as alternatives to the corresponding transliterations x[x.>3] and x = x[x.>3]. Using filter! reduces the use of temporary arrays.

    与 Python 的显著差异

  • Julia requires end to end a block. Unlike Python, Julia has no pass keyword.

  • 在 Julia 中，数组、字符串等的索引从 1 开始，而不是从 0 开始。
  • Julia's slice indexing includes the last element, unlike in Python. a[2:3] in Julia is a[1:3] in Python.
  • Julia does not support negative indices. In particular, the last element of a list or array is indexed with end in Julia, not -1 as in Python.
  • Julia's for, if, while, etc. blocks are terminated by the end keyword. Indentation level is not significant as it is in Python.
  • Julia has no line continuation syntax: if, at the end of a line, the input so far is a complete expression, it is considered done; otherwise the input continues. One way to force an expression to continue is to wrap it in parentheses.
  • Julia arrays are column major (Fortran ordered) whereas NumPy arrays are row major (C-ordered) by default. To get optimal performance when looping over arrays, the order of the loops should be reversed in Julia relative to NumPy (see relevant section of Performance Tips).
  • Julia's updating operators (e.g. +=, -=, …) are not in-place whereas NumPy's are. This means A = [1, 1]; B = A; B += [3, 3] doesn't change values in A, it rather rebinds the name B to the result of the right-hand side B = B + 3, which is a new array. For in-place operation, use B .+= 3 (see also dot operators), explicit loops, or InplaceOps.jl.
  • Julia evaluates default values of function arguments every time the method is invoked, unlike in Python where the default values are evaluated only once when the function is defined. For example, 每次无输入参数调用时，函数f(x=rand()) = x都返回一个新的随机数 On the other hand, the function g(x=[1,2]) = push!(x,3) returns [1,2,3] every time it is called as g().

  • In Julia % is the remainder operator, whereas in Python it is the modulus.

    与 C/C++ 的显著差异

  • Julia 的数组由方括号索引，方括号中可以包含不止一个维度 A[i,j]。 这样的语法不仅仅是像 C/C++ 中那样对指针或者地址引用的语法糖，参见 Julia 文档数组构造的语法（依版本不同有所变动）。

  • 在 Julia 中，数组、字符串等的索引从 1 开始，而不是从 0 开始。
  • Julia 的数组在赋值给另一个变量时不发生复制。执行 A = B 后，改变 B 中元素也会修改 A。像 += 这样的更新运算符不会以 in-place 的方式执行，而是相当于 A = A + B，将左侧绑定到右侧表达式的计算结果上。 as well. Updating operators like += do not operate in-place, they are equivalent to A = A + B which rebinds the left-hand side to the result of the right-hand side expression.
  • Julia 的数组是行优先的（Fortran 顺序），而 C/C++ 的数组默认是列优先的。要使数组上的循环性能最优，在 Julia 中循环的顺序应该与 C/C++ 相反（参见 性能建议）。 default. To get optimal performance when looping over arrays, the order of the loops should be reversed in Julia relative to C/C++ (see relevant section of Performance Tips).
  • Julia 的值在赋值或向函数传递时不发生复制。如果某个函数修改了数组，这一修改 对调用者是可见的。
  • 在 Julia 中，空格是有意义的，这与 C/C++ 不同，所以向 Julia 程序中添加或删除空格时必须谨慎。 whitespace from a Julia program.
  • 在 Julia 中，没有小数点的数值字面量（如 42）生成有符号整数，类型为 Int，但如果字面量太长，超过了机器字长，则会被自动提升为容量更大的类型，例如 Int64（如果 Int 是 Int32）、Int128，或者任意精度的 BigInt 类型。不存在诸如 L, LL, U, UL, ULL 这样的数值字面量后缀指示无符号和/或有符号与无符号。十进制字面量始终是有符号的，十六进制字面量（像 C/C++ 一样由 0x 开头）是无符号的。另外，十六进制字面量与 C/C++/Java 不同， Int, but literals too large to fit in the machine word size will automatically be promoted to a larger size type, such as Int64 (if Int is Int32), Int128, or the arbitrarily large 也与 Julia 中的十进制字面量不同，它们的类型取决于字面量的长度，包括开头的 0。例如，0x0 和 0x00 的类型是 UInt8，0x000 和 0x0000 的类型是 UInt16。同理，字面量的长度在 5-8 之间，类型为 UInt32；在 9-16 之间，类型为 UInt64；在 17-32 之间，类型为 UInt128。当定义十六进制掩码时，就需要将这一问题考虑在内，比如 ~0xf == 0xf0 与 ~0x000f == 0xfff0 完全不同。 unsigned and/or signed vs. unsigned. Decimal literals are always signed, and hexadecimal literals (which start with 0x like C/C++), are unsigned. Hexadecimal literals also, unlike C/C++/Java and unlike decimal literals in Julia, have a type based on the length of the literal, including leading 0s. For example, 0x0 and 0x00 have type UInt8, 0x000 and 0x0000 have type UInt16, then literals with 5 to 8 hex digits have type UInt32, 9 to 16 hex digits type UInt64 and 17 to 32 hex digits type UInt128. This needs to be taken into account when defining hexadecimal masks, for example ~0xf == 0xf0 is very different from ~0x000f == 0xfff0. 64 bit Float64 and 32 bit Float32 bit literals are expressed as 1.0 and 1.0f0 respectively. Floating point literals are rounded (and not promoted to the BigFloat type) if they can not be exactly represented. Floating point literals are closer in behavior to C/C++. Octal (prefixed with 0o) and binary (prefixed with 0b) literals are also treated as unsigned.
  • String literals can be delimited with either " or """, """ delimited literals can contain " characters without quoting it like "\"" String literals can have values of other variables or expressions interpolated into them, indicated by $variablename or $(expression), which evaluates the variable name or the expression in the context of the function.
  • // indicates a Rational number, and not a single-line comment (which is # in Julia)

  • = indicates the start of a multiline comment, and =# ends it.

  • Functions in Julia return values from their last expression(s) or the return keyword. Multiple values can be returned from functions and assigned as tuples, e.g. (a, b) = myfunction() or a, b = myfunction(), instead of having to pass pointers to values as one would have to do in C/C++ (i.e. a = myfunction(&b).
  • Julia does not require the use of semicolons to end statements. The results of expressions are not automatically printed (except at the interactive prompt, i.e. the REPL), and lines of code do not need to end with semicolons. println or @printf"">@printf can be used to print specific output. In the REPL, ; can be used to suppress output. ; also has a different meaning within [ ], something to watch out for. ; can be used to separate expressions on a single line, but are not strictly necessary in many cases, and are more an aid to readability.
  • In Julia, the operator ⊻ (xor) performs the bitwise XOR operation, i.e. ^ in C/C++. Also, the bitwise operators do not have the same precedence as C/++, so parenthesis may be required.
  • Julia's ^ is exponentiation (pow), not bitwise XOR as in C/C++ (use ⊻, or xor, in Julia)
  • Julia has two right-shift operators, >> and >>>. >>> performs an arithmetic shift, >> always performs a logical shift, unlike C/C++, where the meaning of >> depends on the type of the value being shifted.
  • Julia's -> creates an anonymous function, it does not access a member via a pointer.
  • Julia does not require parentheses when writing if statements or for/while loops: use for i in [1, 2, 3] instead of for (int i=1; i <= 3; i++) and if i == 1 instead of if (i == 1).
  • Julia does not treat the numbers 0 and 1 as Booleans. You cannot write if (1) in Julia, because if statements accept only booleans. Instead, you can write if true, if Bool(1), or if 1==1.
  • Julia uses end to denote the end of conditional blocks, like if, loop blocks, like while/ for, and functions. In lieu of the one-line if ( cond ) statement, Julia allows statements of the form if cond; statement; end, cond && statement and !cond || statement. Assignment statements in the latter two syntaxes must be explicitly wrapped in parentheses, e.g. cond && (x = value), because of the operator precedence.
  • Julia has no line continuation syntax: if, at the end of a line, the input so far is a complete expression, it is considered done; otherwise the input continues. One way to force an expression to continue is to wrap it in parentheses.
  • Julia macros operate on parsed expressions, rather than the text of the program, which allows them to perform sophisticated transformations of Julia code. Macro names start with the @ character, and have both a function-like syntax, @mymacro(arg1, arg2, arg3), and a statement-like syntax, @mymacro arg1 arg2 arg3. The forms are interchangeable; the function-like form is particularly useful if the macro appears within another expression, and is often clearest. The statement-like form is often used to annotate blocks, as in the distributed for construct: @distributed for i in 1:n; #= body =#; end. Where the end of the macro construct may be unclear, use the function-like form.
  • Julia now has an enumeration type, expressed using the macro @enum(name, value1, value2, …) For example: @enum(Fruit, banana=1, apple, pear)
  • By convention, functions that modify their arguments have a ! at the end of the name, for example push!.
  • In C++, by default, you have static dispatch, i.e. you need to annotate a function as virtual, in order to have dynamic dispatch. On the other hand, in Julia every method is "virtual" (although it's more general than that since methods are dispatched on every argument type, not only this, using the most-specific-declaration rule).

原文: https://juliacn.github.io/JuliaZH.jl/latest/manual/noteworthy-differences/