logarithm

I've got a fixed point class (10.22) and I have a need of a pow, a sqrt, an exp and a log function. Alas I have no idea where to even start on this. Can anyone provide me with some links to useful articles or, better yet, provide me with some code? I'm assuming that once I have an exp function then it becomes relatively easy to implement pow and sqrt as they just become. pow( x, y ) => exp( y * log( x ) ) sqrt( x ) => pow( x, 0.5 ) Its just those exp and log functions that I'm finding difficult (as though I remember a few of my log rules, I can't remember much else about them). Presumably,

I have a slider with values ranging from 0 to 100. I want to map them to a range from 100 to 10,000,000. I've seen some functions scattered around the net but they're all in C++. I need it in Javascript. Any ideas? You can use a function like this: function logslider(position) { // position will be between 0 and 100 var minp = 0; var maxp = 100; // The result should be between 100 an 10000000 var minv = Math.log(100); var maxv = Math.log(10000000); // calculate adjustment factor var scale = (maxv-minv) / (maxp-minp); return Math.exp(minv + scale*(position-minp)); } The resulting values match a

I use the following function to calculate log base 2 for integers: public static int log2(int n){ if(n <= 0) throw new IllegalArgumentException(); return 31 - Integer.numberOfLeadingZeros(n); } Does it have optimal performance? Does someone know ready J2SE API function for that purpose? UPD1 Surprisingly for me, float point arithmetics appears to be faster than integer arithmetics. UPD2 Due to comments I will conduct more detailed investigation. UPD3 My integer arithmetic function is 10 times faster than Math.log(n)/Math.log(2). If you are thinking about using floating-point to help with

I'm trying to scale the x axis of a plot with math.log(1+x) instead of the usual 'log' scale option, and I've looked over some of the custom scaling examples but I can't get mine to work! Here's my MWE: import matplotlib.pyplot as plt import numpy as np import math from matplotlib.ticker import FormatStrFormatter from matplotlib import scale as mscale from matplotlib import transforms as mtransforms class CustomScale(mscale.ScaleBase): name = 'custom' def __init__(self, axis, **kwargs): mscale.ScaleBase.__init__(self) self.thresh = None #thresh def get_transform(self): return self

Say I have the following variables and its corresponding values which represents a record . name = 'abc' age = 23 weight = 60 height = 174 Please note that the value could be of different types ( string , integer , float , reference-to-any-other-object, etc). There will be many records (at least >100,000). Each and every record will be unique when all these four variables (actually its values ) are put together. In other words, there exists no record with all 4 values are the same. I am trying to find an efficient data structure in Python which will allow me to (store and) retrieve records

I have a linear scale that ranges form 0.1 to 10 with increments of change at 0.1: |----------[]----------| 0.1 5.0 10 However, the output really needs to be: |----------[]----------| 0.1 1.0 10 (logarithmic scale) I'm trying to figure out the formula needed to convert the 5 (for example) to 1.0. Consequently, if the dial was shifted halfway between 1.0 and 10 (real value on linear scale being 7.5), what would the resulting logarithmic value be? Been thinking about this for hours, but I have not worked with this type of math in quite a few years, so I am really lost. I understand the basic

I have a BigInteger number, for example beyond 2 64 . Now i want to calculate the logarithm of that BigInteger number, but the method BigInteger.log() does not exist. How do I calculate the (natural) logarithm of my large BigInteger value? If you want to support arbitrarily big integers, it's not safe to just do Math.log(bigInteger.doubleValue()); because this would fail if the argument exceeds the double range (about 2^1024 or 10^308, i.e. more than 300 decimal digits ). Here's my own class that provides the methods double logBigInteger(BigInteger val); double logBigDecimal(BigDecimal val);