This means that for numbers that require an infinite number of bits to represent exactly, such as irrational numbers like pi, the computer can only represent an approximation of the number. Modern computers use finite-precision arithmetic to perform computations, which means that they can only represent numbers with a limited number of bits. I cound not answer ur problem and look around for a bit and found this: I also recommend this explanation of floating point representation: Try clicking the 1s or 0s to see what happens when they take on different values. It currently represents 0.75 in 64-bit floating point. I find it helpful to see what happens when you change bits in a representation. If it goes above that, then the exponent can be increased instead. The key thing here is that the mantissa only ever needs to be between 1.0 and 2.0 (excluding 2). In this case, there is a 1 in the first bit, so this mantissa is 1.5. The goal is for those bits to be able to represent values between 0 and 1, which then is considered a mantissa between 1 and 2. According to the floating point standard, the first bit represents 1/2 (0.5), the second bit represents 1/4, the third bit represents 1/8, etc. 1022-1023 is -1, which is indeed the exponent. According to the floating point standard, the exponent is calculated by subtracting 1023 from that value. The next 11 bits represents the exponent -1: The first bit represents the sign, where 0 is positive. The floating point representation of 0.750 in binary needs to include the sign (positive/negative), the mantissa, and the exponent. The number -1 is called the "exponent" (as per normal math term). The number 1.5 is called either the "mantissa" or the "significand". Let's step through how the computer actually represents that number in floating point representation and see if that helps. In general, error messages tell you where the problem was discovered, but that is often not where it was caused.From the author: Apologies for the confusing explanation. The solution is to represent signal power and noise power with floatingpoint values. The problem is in line 4, because dividing two integers does floor division. To find the real error, it might be useful to print the value of ratio, which turns The error message indicates line 5, but there is nothing wrong with that line. Traceback (most recent call last): File "snr.py", line 5, in ? decibels = 10 * math.log10(ratio) OverflowError: math range error Import math signal_power = 9 noise_power = 10 ratio = signal_power / noise_power decibels = 10 * math.log10(ratio) print decibelsīut when you run it, you get an error message 1: In Python, you might write something like this: Suppose you are trying to compute a signal-tonoise ratio in decibels. Problem was discovered, but the actual error might be earlier in the code, sometimes on a previous line. In general, error messages indicate where the But the error message points to y, which is misleading. In this example, the problem is that the second line is indented by one space. > x = 5 > y = 6 File "", line 1 y = 6 ˆ Synta圎rror: invalid syntax Whitespace errors can be tricky because spaces and tabs are invisible and we are used to ignoring them. Syntax errors are usually easy to find, but there are a few gotchas.
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