Optimization and Fit in SciPy – scipy.optimize Optimization provides a useful algorithm for minimization of curve fitting, multidimensional or scalar and root fitting. Let’s take an example of a Scalar Function, to find minimum scalar function %matplotlib inline import matplotlib.pyplot as plt from scipy import optimize import numpy as np def function(a): return a*2 + 20 * np.sin(a) plt.plot(a, function(a)) plt.show() #use BFGS algorithm for optimization optimize.fmin_bfgs(function, 0) Output: Optimization terminated successfully. Current function value: -23.241676 Iterations: 4 Function evaluations: 18 Gradient evaluations: 6 array([-1.67096375]) In this example, optimization is done with the help of the gradient descent algorithm from the initial…
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Discrete Fourier Transform – scipy.fftpack DFT is a mathematical technique which is used in converting spatial data into frequency data. FFT (Fast Fourier Transformation) is an algorithm for computing DFT FFT is applied to a multidimensional array. Frequency defines the number of signal or wavelength in particular time period. Example: Take a wave and show using Matplotlib library. we take simple periodic function example of sin(20 × 2πt) %matplotlib inline from matplotlib import pyplot as plt import numpy as np #Frequency in terms of Hertz fre = 5 #Sample rate fre_samp = 50 t = np.linspace(0, 2, 2 * fre_samp,…
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Linear Algebra with SciPy Linear Algebra of SciPy is an implementation of BLAS and ATLAS LAPACK libraries. Performance of Linear Algebra is very fast compared to BLAS and LAPACK. Linear algebra routine accepts two-dimensional array object and output is also a two-dimensional array. Now let’s do some test with scipy.linalg, Calculating determinant of a two-dimensional matrix, from scipy import linalg import numpy as np #define square matrix two_d_array = np.array([ [4,5], [3,2] ]) #pass values to det() function linalg.det( two_d_array ) Output: -7.0 Inverse Matrix – scipy.linalg.inv() Inverse Matrix of Scipy calculates the inverse of any square matrix. Let’s see, from scipy import…
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Special Function package scipy.special package contains numerous functions of mathematical physics. SciPy special function includes Cubic Root, Exponential, Log sum Exponential, Lambert, Permutation and Combinations, Gamma, Bessel, hypergeometric, Kelvin, beta, parabolic cylinder, Relative Error Exponential, etc.. For one line description all of these function, type in Python console: help(scipy.special) Output : NAME scipy.special DESCRIPTION ======================================== Special functions (:mod:`scipy.special`) ======================================== .. module:: scipy.special Nearly all of the functions below are universal functions and follow broadcasting and automatic array-looping rules. Exceptions are noted. Cubic Root Function: Cubic Root function finds the cube root of values. Syntax: scipy.special.cbrt(x) Example: from scipy.special import cbrt #Find…
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File Input / Output package: Scipy, I/O package, has a wide range of functions for work with different files format which are Matlab, Arff, Wave, Matrix Market, IDL, NetCDF, TXT, CSV and binary format. Let’s we take one file format example as which are regularly use of MatLab: import numpy as np from scipy import io as sio array = np.ones((4, 4)) sio.savemat('example.mat', {'ar': array}) data = sio.loadmat(‘example.mat', struct_as_record=True) data['ar'] Output: array([[ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.]]) Code Explanation Line 1 & 2: Import the essential library…
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Numpy VS SciPy Numpy: Numpy is written in C and use for mathematical or numeric calculation. It is faster than other Python Libraries Numpy is the most useful library for Data Science to perform basic calculations. Numpy contains nothing but array data type which performs the most basic operation like sorting, shaping, indexing, etc. SciPy: SciPy is built in top of the NumPy SciPy is a fully-featured version of Linear Algebra while Numpy contains only a few features. Most new Data Science features are available in Scipy rather than Numpy. Functions Ideally speaking, NumPy is basically for basic operations…
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SciPy, pronounced as Sigh Pi, is a scientific python open source, distributed under the BSD licensed library to perform Mathematical, Scientific and Engineering Computations. SciPy is built in top of the NumPy SciPy is a fully-featured version of Linear Algebra while Numpy contains only a few features. Most new Data Science features are available in Scipy rather than Numpy. SciPy works efficiently on NumPy arrays and is standard scientific computing library in Python. SciPy library is composed of sub-modules designed for specific tasks. File input/output – scipy.io Special Function – scipy.special Linear Algebra Operation – scipy.linalg Interpolation – scipy.interpolate Optimization and fit – scipy.optimize Statistics…
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Python is a very popular language. It’s also one of the languages that I recommend for beginners to start with. But how do you go about learning this language? The best way to learn Python is to understand the big picture of all what you need to learn before you dive in and start learning. In this article, I divide the path of learning Python into 6 levels. Each level covers a subset of the language that you need to master before you move on to the next one. My focus on this article is for you to be a competent well-rounded programmer so you…
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In this tutorial, we are going to see how to use Retrofit HTTP client in your Android application. Retrofit is an awesome type-safe HTTP client for Android and Java built by awesome folks at Square. Retrofit makes it easy to consume JSON or XML data which is parsed into Plain Old Java Objects (POJOs). So, without any further delays, lets get started by first creating a new project in Android Studio. 1.Go to File ⇒ New Project. When it prompts you to select the default activity, select Empty Activity and proceed. 2.Open build.gradle in (Module:app) and add Retrofit, Picasso, RecyclerView, Gson dependencies like this. dependencies { ... compile "com.android.support:cardview-v7:26.1.0" compile…
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echo "Enter Content" touch contentfile end="end" while true do read content if [ "$content" != "$end" ] then echo $content >> contentfile line=$((line + 1)) else break fi done echo "-----------------------------------------" echo " Content Of File" echo "Name Of File :: contentfile" echo "Number Of Line $line" read data < contentfile while read data do echo "$data" done < contentfile echo "-----------------------------------------"