By Michael Snoyman
This fast-moving advisor introduces internet program improvement with Haskell and Yesod, a powerful language/framework mix that helps high-performing functions which are modular, type-safe, and concise. You’ll paintings with a number of samples to discover the best way Yesod handles widgets, types, endurance, and RESTful content material. you furthermore mght get an advent to numerous Haskell instruments to complement your easy wisdom of the language.
By the time you end this ebook, you’ll create a production-quality internet program with Yesod’s ready-to-use scaffolding. You’ll additionally research numerous real-world examples, together with a weblog, a wiki, a JSON net carrier, and a Sphinx seek server.
* construct an easy program to profit Yesod’s origin datatype and net program Interface (WAI)
* Produce cleanser, extra modular code via studying how Yesod monads have interaction
* enforce the yesod-form declarative API to construct kinds on best of widgets
* find out how Yesod and Haskell shop consultation info and deal with endurance
* Use options to serve an HTML web page and a machine-friendly JSON web page from a similar URL
* Create reusable elements for numerous purposes with Yesod subsites
Read or Download Developing Web Applications with Haskell and Yesod PDF
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Extra resources for Developing Web Applications with Haskell and Yesod
This explains why the sum is equal to 1/(1 − K). 19 20 CH A P T E R 0: From the Ground Up! 4 COMPLEX OR REAL? Most of the theory of signals and systems is based on functions of a complex variable. Clearly, signals are functions of a real variable corresponding to time or space (if the signal is two-dimensional, like an image) so why would one need complex numbers in processing signals? As we will see later, time-dependent signals can be characterized by means of frequency and damping. These two characteristics are given by complex variables such as s = σ + j (where σ is the damping factor and is the frequency) in the representation of analog signals in the Laplace transform, or z = rejω (where r is the damping factor and ω is the discrete frequency) in the representation of discrete-time signals in the Z-transform.
01 for each case instead of 1 as in the case when Ts = 1. Thus, whenever the rate of change of the signal is faster, the difference gets closer to the derivative by making Ts smaller. It becomes clear that the faster the signal changes, the smaller the sampling period Ts should be in order to get a better approximation of the signal and its derivative. As we will learn in Chapters 4 and 5 the frequency content of a signal depends on the signal variation over time. A constant signal has frequency zero, while a signal that changes very fast over time would have high frequencies.
The first one is where the entries of one vector are multiplied by the corresponding entries of the other. ∗x % multiplication of entries of two vectors v= 1 4 9 16 The other type of multiplication is the conventional multiplication allowed in linear algebra. For instance, with that of a row vector by a column vector, w = x∗x’ % multiplication of x (row vector) by x’(column vector) w = 30 the result is a constant—in this case, the length of the row vector should coincide with that of the column vector.