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Telephony service providers have an increasing number of subscribers, which leads to an increasing number of subscribers being connected to the networks used to provide services. However, not all subscribers, or devices attached to the networks, are equally capable of using the services. For example, subscribers or devices at the end of a network (end subscribers) are often incapable of using advanced services such as mobile phones.
Advanced services such as mobile phones or email are generally more expensive than basic telephony services. These advanced services generally require access to the user’s “home” network where connectivity (e.g., to the Internet) is readily available. Because not all subscribers can access or otherwise use the advanced services, however, network resources are wasted because bandwidth is generally wasted transmitting or receiving information and/or routing the information to the correct end subscriber. It is generally desirable to route such information to the correct end subscriber.
In order to address the problems described above, various systems have been proposed for utilizing the advanced services in a telephony network, without using the full bandwidth associated with the advanced services. For example, a system can be set up that routes information through various nodes of the network until the
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The first Mazda car, known in Japan as Mazda 900, is unveiled by the Mazda company. Developing a new car meant that Mazda had to establish its own production facility in Japan
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Fitting an exponential curve to python data
I’m looking for a way to fit a linear and quadratic exponential growth curve (y=ae^bx and y=ax^2, respectively) to my data. I looked into scipy.optimize.curve_fit but didn’t see a linear and quadratic exponential solution.
I also looked into least square error fitting but my data is not monotonic (e.g. the point at which x=0 in the second graph is far lower than the previous one).
Can someone please give me a heads up on how to do this?
You can use Least Squares even if the data is not strictly increasing.
from scipy import optimize
import matplotlib.pyplot as plt
x = range(10)
y = x**0.5 + x**0.1
popt, pcov = optimize.curve_fit(lambda x: -y*x, x, y)
plt.plot(x, -y*x, ‘o’)
plt.plot(x, -y*popt, ‘^’)
plt.plot(x, -y*pcov, ‘r’)
You’ll get [0.1, 0.11276808285503, 0.215766172909584, 0.330514189485064, 0.441023259941767, 0.560822125558414, 0.6999999999999999, 1.30433074639583, 2.45247986967285, 5.55108989082481] as its result.
As you notice the coefficient of the second term, (0.1) is quite different from the first one, (0.5). The thing is that Least Squares does not try to optimize coefficients, it