Although MATLAB users may find Julia’s syntax familiar, Julia is not a MATLAB clone. There are major syntactic and functional differences. The following are some noteworthy differences that may trip up Julia users accustomed to MATLAB:
A[i,j].A=B, changing elements of B will modify A as well.a(4) = 3.2 can create the array a = [0 0 0 3.2] and a(5) = 7 can grow it into a = [0 0 0 3.2 7], the corresponding Julia statement a[5] = 7 throws an error if the length of a is less than 5 or if this statement is the first use of the identifier a. Julia has push!() and append!(), which grow Vectors much more efficiently than MATLAB’s a(end+1) = val.sqrt(-1) is represented in Julia as im, not i or j as in MATLAB.42) create integers instead of floating point numbers. Arbitrarily large integer literals are supported. As a result, some operations such as 2^-1 will throw a domain error as the result is not an integer (see the FAQ entry on domain errors for details).(a, b) = (1, 2) or a, b = 1, 2. MATLAB’s nargout, which is often used in MATLAB to do optional work based on the number of returned values, does not exist in Julia. Instead, users can use optional and keyword arguments to achieve similar capabilities.N, not Nx1. For example, rand(N) makes a 1-dimensional array.[x,y,z] concatenates in the first dimension (“vertically”). For concatenation in the second dimension (“horizontally”), use spaces as in [x y z]. To construct block matrices (concatenating in the first two dimensions), the syntax [a b; c d] is used to avoid confusion. In Julia v0.4, the concatenation syntax [x, [y, z]] is deprecated in favor of [x; [y, z]].a:b and a:b:c construct Range objects. To construct a full vector like in MATLAB, use collect(a:b). Generally, there is no need to call collect though. Range will act like a normal array in most cases but is more efficient because it lazily computes its values. This pattern of creating specialized objects instead of full arrays is used frequently, and is also seen in functions such as linspace, or with iterators such as enumerate, and zip. The special objects can mostly be used as if they were normal arrays.return keyword instead of listing the names of variables to return in the function definition (see The return Keyword for details).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.sort() that operate column-wise by default (sort(A) is equivalent to sort(A,1)) do not have special behavior for 1xN arrays; the argument is returned unmodified since it still performs sort(A,1). To sort a 1xN matrix like a vector, use sort(A,2).A is a 2-dimensional array, fft(A) computes a 2D FFT. In particular, it is not equivalent to fft(A,1), which computes a 1D FFT acting column-wise.tic() and toc().println() or @printf() can be used to print specific output.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 =.&, |, and $ 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))...., as in xs=[1,2]; f(xs...).svd() returns singular values as a vector instead of as a dense diagonal matrix.... is not used to continue lines of code. Instead, incomplete expressions automatically continue onto the next line.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.types do not support dynamically adding fields at runtime, unlike MATLAB’s classes. Instead, use a Dict.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.vertcat(A{:}) in MATLAB, is written using the splat operator in Julia, e.g. as vcat(A...).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:
""" ... """. This syntax is convenient for constructing strings that contain line breaks...., which always follows the name of a specific variable, unlike R, for which ... can occur in isolation.mod(a, b), not a %% b. % in Julia is the remainder operator.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].c(1,2,3,4) + c(1,2) is valid R but the equivalent [1:4] + [1:2] will throw an error in Julia.apply() takes the function first, then its arguments, unlike lapply(<structure>, function, arg2, ...) in R.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).<-, <<- and -> are not assignment operators.-> creates an anonymous function, like Python.[1, 2, 3] is the equivalent of R’s c(1, 2, 3).* 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..' operator and conjugated transposition using the ' operator. Julia’s A.' is therefore equivalent to R’s t(A).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).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.nrow and ncol. Instead, use size(M, 1) for nrow(M) and size(M, 2) for ncol(M).1 and c(1) are the same. In Julia, they can not be used interchangeably. One potentially confusing result of this is that x' * y for vectors x and y is a 1-element vector, not a scalar. To get a scalar, use dot(x, y).diag() and diagm() are not like R’s.diag(M) = ones(n).list(a = 1, b = 2), use (1, 2).table(x::TypeA) and table(x::TypeB) act like R’s table.TypeA(x) and table.TypeB(x).hcat(), vcat() and hvcat(), not c, rbind and cbind like in R.a:b is not shorthand for a vector like in R, but is a specialized Range that is used for iteration without high memory overhead. To convert a range into a vector, use collect(a:b).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.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, 1) is a row vector containing the sum over each column and sum(A, 2) is a column vector containing the sum over each row. This contrasts to the behavior of R, where sum(B,1)=11 and sum(B,2)=12. If the second argument is a vector, then it specifies all the dimensions over which the sum is performed, e.g., sum(A,[1,2])=10. It should be noted that there is no error checking regarding the second argument.sort() and sort!().NULL type.assign or get.return does not require parentheses.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.[1, [2, 3]] concatenates into [1, 2, 3], like in R.Int[1, Int[2, 3]] will not concatenate, but instead throw an error.Any[1, [2,3]] will not concatenate.Vector{Int}[[1, 2], [3, 4]] will not concatenate, but produces an object similar to Python’s list of lists. This object is different from a two-dimensional Array of Ints.end to end a block. Unlike Python, Julia has no pass keyword.a[2:3] in Julia is a[1:3] in Python.end in Julia, not -1 as in Python.if clause that Python has.for, if, while, etc. blocks are terminated by the end keyword. Indentation level is not significant as it is in Python.+=, -=, ...) are not in-place whereas NumPy’s are. This means A = ones(4); B = A; B += 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. Use B[:] += 3, explicit loops, or InplaceOps.jl.f(x=rand()) = x returns a new random number every time it is invoked without argument. On the other hand, the function g(x=[1,2]) = push!(x,3) returns [1,2,3] every time it is called as g().% is the remainder operator, whereas in Python it is the modulus.A[i,j]. This syntax is not just syntactic sugar for a reference to a pointer or address as in C/C++. See the Julia documentation for the syntax for array construction (it has changed between versions).A=B, changing elements of B will modify A 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.42) create signed integers, of type 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 BigInt type. There are no numeric literal suffixes, such as L, LL, U, UL, ULL to indicate 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." 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 $(expression), which evaluates the variable name or the expression in the context of the function.// indicates a Rational number, and not a single-line comment (which is # in Julia)#= indicates the start of a multiline comment, and =# ends it.return keyword. Multiple values can be returned from functions and assigned as tuples, e.g. (a, b) = myfunction() or a, b = myfunction(), instead of having to pass pointers to values as one would have to do in C/C++ (i.e. a = myfunction(&b).println() or @printf() can be used to print specific output. In the REPL, ; can be used to suppress output. ; also has a different meaning within [ ], something to watch out for. ; can be used to separate expressions on a single line, but are not strictly necessary in many cases, and are more an aid to readability.$ 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.^ is exponentiation (pow), not bitwise XOR as in C/C++ (use $ in Julia)>> 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.-> creates an anonymous function, it does not access a member via a pointer.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).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.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.@ character, and have both a function-like syntax, @mymacro(arg1, arg2, arg3), and a statement-like syntax, @mymacro arg1 arg2 arg3. The forms are interchangable; 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 parallel for construct: @parallel for i in 1:n; #= body =#; end. Where the end of the macro construct may be unclear, use the function-like form.@enum(name, value1, value2, ...) For example: @enum(Fruit, Banana=1, Apple, Pear)
! at the end of the name, for example push!.this, using the most-specific-declaration rule).
© 2009–2016 Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors
Licensed under the MIT License.
http://docs.julialang.org/en/release-0.5/manual/noteworthy-differences/