If you are trying to predict one value from the other two, then you should use lstsq with the a argument as your independent variables (plus a column of 1’s to estimate an intercept) and b as your dependent variable. If, on the other hand, you just want to get the best fitting line to … Read more
arange generates lists (well, numpy arrays); type help(np.arange) for the details. You don’t need to call it on existing lists. >>> x = [1,2,3,4] >>> y = [3,5,7,9] >>> >>> m,b = np.polyfit(x, y, 1) >>> m 2.0000000000000009 >>> b 0.99999999999999833 I should add that I tend to use poly1d here rather than write out … Read more
To answer your questions directly: Yes. Catmull-Rom spline is an algorithm to interpolate a series of (x, y, z) points. It will generate a cubic polynomial curve between each two consecutive points. You cannot direcly use Catmull Rom spline for SVG path. You need to convert it to cubic Bezier curve first. For a curve … Read more
You can pass curve_fit a multi-dimensional array for the independent variables, but then your func must accept the same thing. For example, calling this array X and unpacking it to x, y for clarity: import numpy as np from scipy.optimize import curve_fit def func(X, a, b, c): x,y = X return np.log(a) + b*np.log(x) + … Read more
To get a third order polynomial in x (x^3), you can do lm(y ~ x + I(x^2) + I(x^3)) or lm(y ~ poly(x, 3, raw=TRUE)) You could fit a 10th order polynomial and get a near-perfect fit, but should you? EDIT: poly(x, 3) is probably a better choice (see @hadley below).
Here is a parameter-free fitting function fit_sin() that does not require manual guess of frequency: import numpy, scipy.optimize def fit_sin(tt, yy): ”’Fit sin to the input time sequence, and return fitting parameters “amp”, “omega”, “phase”, “offset”, “freq”, “period” and “fitfunc””’ tt = numpy.array(tt) yy = numpy.array(yy) ff = numpy.fft.fftfreq(len(tt), (tt[1]-tt[0])) # assume uniform spacing Fyy … Read more
I suggest you to start with simple polynomial fit, scipy.optimize.curve_fit tries to fit a function f that you must know to a set of points. This is a simple 3 degree polynomial fit using numpy.polyfit and poly1d, the first performs a least squares polynomial fit and the second calculates the new points: import numpy as … Read more
You can use numpy.piecewise() to create the piecewise function and then use curve_fit(), Here is the code from scipy import optimize import matplotlib.pyplot as plt import numpy as np %matplotlib inline x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ,11, 12, 13, 14, 15], dtype=float) y = np.array([5, 7, 9, 11, … Read more
An easy solution, as you mentioned, is to simply connect the points with a straight line. Here’s the code to do so: public void onDraw(Canvas canvas) { Path path = new Path(); boolean first = true; for(Point point : points){ if(first){ first = false; path.moveTo(point.x, point.y); } else{ path.lineTo(point.x, point.y); } } canvas.drawPath(path, paint); } … Read more
If I understand your question correctly, then you probably want a density estimate along with the histogram: X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)) hist(X, prob=TRUE) # prob=TRUE for probabilities not counts lines(density(X)) # add a density estimate with defaults lines(density(X, adjust=2), lty=”dotted”) # add another “smoother” density Edit a long while … Read more