2D / 1D - mapping is pretty simple. Given x and y, and 2D array sizes width (for x-direction) and height (for y-direction), you can calculate the according index i in 1D space (zero-based) by
i = x + width*y;
and the reverse operation is
x = i % width; // % is the "modulo operator", the remainder of i / width;
y = i / width; // where "/" is an ...
You need to keep in mind that in FPU arithmetics, 0 doesn't necessarily has to mean exactly zero, but also value too small to be represented using given datatype, e.g.
a = -1 / 1000000000000000000.0
a is too small to be represented correctly by float (32 bit), so it is "rounded" to -0.
Now, let's say our computation continues:
b = 1 / a
Because a is ...
The CPU has built in detection. Most instruction set architectures specify that the CPU will trap to an exception handler for integer divide by zero (I don't think it cares if the dividend is zero).
It is possible that the check for a zero divisor happens in parallel in hardware along with the attempt to do the division, however, the detection of the ...
Two common cases to consider:
Obviously if you are using integer arithmetic (which truncates) you will get a different result. Here's a small example in C#:
public static void TestIntegerArithmetic()
int newValue = 101;
int oldValue = 10;
int SOME_CONSTANT = 10;
if(newValue / oldValue > SOME_CONSTANT)
Assuming that your square might be rotated against whatever coordinates system you have in place, you can't rely on there being any repetition of X and Y values in your four points.
What you can do is calculate the distances between each of the four points. If you find the following to be true, you have a square:
There are two points, say A and C which ...
Suppose we're designing a new language and we want Sqrt to be an instance method. So we look at the double class and begin designing. It obviously has no inputs (other than the instance) and returns a double. We write and test the code. Perfection.
But taking the square root of an integer is valid, too, and we don't want to force everyone to convert to ...
Although there were older precursors, the influential French mathematician Rene Descartes is usually credited for introducing superscripted exponents (ab) into mathematical writing, in his work Geometrie which was published in 1637. This is the notation still universally used in mathematics today.
Fortran is the oldest programming language widely used for ...
The usual reason for writing numbers, in code, in other than base 10, is because you're bit-twiddling.
To pick an example in C (because if C is good for anything, it's good for bit-twiddling), say some low-level format encodes a 2-bit and a 6-bit number in a byte: xx yyyyyy:
unsigned char codevalue = 0x94; // 10 010100
According to MDN Math.min accepts only numbers, and if one of the arguments is not a number, it'll return NaN.
That's not what it says (bold emphasis mine):
If at least one of arguments cannot be converted to a number, the result is NaN.
Math.min uses ToNumber to convert its arguments.
ToNumber uses ToPrimitive to convert Objects (and ...
Your argumentation against floating point numbers is very fragile,
probably because of naivety. (No offense here, I find your question is
actually very interesting, I hope my answer will also be.)
A classic argument is that floats provide a greater range, but high
precision integers can meet this challenge now. For example: with
modern 64-bit ...
The main reason I use different bases is when I care about bits.
It's much easier to read
byte bottom_byte = value & mask;
byte bottom_byte = value & mask;
Or image something more complex
int top_bytes_by_word = value & mask;
int mask=4278255360; //can you say magic ...
There isn't any, and it's pretty arbitrary.
The only useful distinction is between first class, and all others. Every case that's in the "other" bracket has its own distinct set of rules in each case and lumping them all together just isn't very helpful. "First class" means "You don't have to look up the rules", essentially, and "other" is "You have to ...
This is not a C++ library issue but a question of mathematical terminology. In mathematics, a norm can mean different things:
What you call norm is the Euclidian norm, which is the distance to the origin. In C++ it's abs(). This naming convention has the advantage of being consistent for complex and for real numbers (the origin in the latter case being ...
It depends on the language, on the compiler, on whether you are using integers or floating point numbers, and so on.
For floating point number, most implementations use the IEEE 754 standard, where division by 0 is well defined. 0 / 0 gives a well defined result of NaN (not-a-number), and x / 0 for x ≠ 0 gives either +Infinity or -Infinity, depending on ...
The right thing to do in such circumstances is to implement the algorithm, formula or whatever with exactly the same variable names as in the primary real-world source (as far as the programming language allows this), and have a succinct comment above it saying something like "Levenshtein distance computation as described in [Knuth1968]", where the citation ...
As a Computer Scientist looking to get a Master's degree with focus on "Algorithms, Complexity and Computability Theory and Programming Languages" I would say Discrete Mathematics is very important.
Discrete math will help you with the "Algorithms, Complexity and Computability Theory" part of the focus more than programming language. The understanding of ...
To simplify things by defining a concrete implementation, I will assume (as other answers do) that we're talking about IEEE 754 64-bit floating point.
Each floating point number has three parts: a sign, an exponent, and a mantissa. (Technical details about hidden bits are irrelevant to this discussion).
Reciprocation doesn't affect the sign
1 / (2**e * m) ...
Because switching to integers doesn't solve anything. The problem with floats isn't that they have inaccuracies, it's that half the people using them don't pay any attention to what's going on. Those same people aren't going to pay proper attention to the units they are using when they use an integer, and a different set of screw ups will happen.
Mathematical operations are often very performance-sensitive. Therefore, we will want to use static methods that can be fully resolved (and optimizied, or inlined) at compile time. Some languages do not offer any mechanism to specify statically dispatched methods. Furthermore, the object model of many languages has considerable memory overhead that is ...
I like your question as it potentially covers many ideas. On the whole, I suspect the answer is it depends, probably on the types involved and the possible range of values in your specific case.
My initial instinct is to reflect on the style, ie. your new version is less clear to the reader of your code. I imagine I would have to think for a second or two (...
Christophe's post, whilst fully correct, does not actually answer the question why the terms look like they do.
To give you definite answer for the reasons, you would have to ask someone from the C++ standard committee, but let me make an "educated guess":
There was already a function name std::abs in use for the euclidean norm for float and double values,...
Pick three of the four points.
Figure out if it's a right isosceles triangle by checking if one of the three vectors between points is equal to another one rotated by 90 degrees.
If so, compute the fourth point by vector addition and compare it to the given fourth point.
Note that this doesn't require expensive square roots, not even multiplication.
This multiplication algorithm does not replace multiplication with addition. Instead, it splits the multiplication into a number of smaller multiplications that are easier for humans to understand. Humans (unlike computers) deal well with patterns and symbols, less so with large numbers (where “large“ means “multiple digits”).
321 × 254
× | 2E2 + 5E1 + ...
I'd probably call that premature optimization, in a broad sense, regardless of whether you're optimizing for performance, as the phrase generally refers to, or anything else that can be optimized, such as edge-count, lines of code, or even more broadly, things like "design."
Implementing that sort of optimization as a standard operating procedure puts ...
Physical characteristics of the universe (like the number of atoms in it) are not useful to determine the boundaries of number sizes, because useful calculations exist using numbers having wider ranges.
Floating point numbers are a tradeoff between accuracy and range. They deliberately give up some accuracy to achieve greater range.
You have one byte, which is 8 bits
So far so good
which is 2^8
Your use of "which is" here seems to be the root of your confusion. A more precise statement is:
8 bits can represent 2^8 distinct values.
N bits can represent 2^N distinct values.
If this is unclear, it may help you to think about decimal digits (0-9). One decimal ...