From 9cc6396ca58196a34f8379be1e2fcad1bb4474a8 Mon Sep 17 00:00:00 2001 From: Ryan C Cooper Date: Wed, 18 Jan 2017 12:31:16 -0500 Subject: [PATCH 1/9] Update lecture_01.md --- lecture_01/lecture_01.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/lecture_01/lecture_01.md b/lecture_01/lecture_01.md index 807e888..196a29e 100644 --- a/lecture_01/lecture_01.md +++ b/lecture_01/lecture_01.md @@ -106,6 +106,5 @@ plot(t,v_analytical,'-',t,v_numerical,'o-') ![plot of velocities](https://github.uconn.edu/rcc02007/ME3255S2017/blob/master/lecture_01/output_10_0.svg) - + From fd8de22fb543e34960c985d033fc290075c157e2 Mon Sep 17 00:00:00 2001 From: Ryan M Hyatt Date: Wed, 18 Jan 2017 21:12:18 -0500 Subject: [PATCH 2/9] Update data.csv Points created using a nerf gun with thumbtacks instead of suction cups (following appropriate range safety standards). Distance rounded to nearest quarter inch using markings on target. Significant to tenth digit. Converted assuming 1 inch to 2.54 cm. --- extra_credit_1/data.csv | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/extra_credit_1/data.csv b/extra_credit_1/data.csv index 8e4a5ce..282ff39 100644 --- a/extra_credit_1/data.csv +++ b/extra_credit_1/data.csv @@ -1,3 +1,8 @@ radius (cm), angle (degrees) 0, 0 2.25, 62 +3.81, 140 +6.35, 116 +.635, 88 +7.62, 12 +1.27, 180 From 34c9847e0ae1e04156c37382578d6fcdfdd75f6d Mon Sep 17 00:00:00 2001 From: Ryan M Hyatt Date: Wed, 8 Feb 2017 11:40:35 -0500 Subject: [PATCH 3/9] Notes on Class Code --- lecture_07/lecture_07.ipynb | 27 ++++++++++++++------------- lecture_07/mod_secant.m | 6 +++--- 2 files changed, 17 insertions(+), 16 deletions(-) diff --git a/lecture_07/lecture_07.ipynb b/lecture_07/lecture_07.ipynb index 08d29c4..66fa9a8 100644 --- a/lecture_07/lecture_07.ipynb +++ b/lecture_07/lecture_07.ipynb @@ -1435,22 +1435,23 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Octave", - "language": "octave", - "name": "octave" + "display_name": "Python [conda root]", + "language": "python", + "name": "conda-root-py" }, "language_info": { - "file_extension": ".m", - "help_links": [ - { - "text": "MetaKernel Magics", - "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" - } - ], - "mimetype": "text/x-octave", - "name": "octave", - "version": "0.19.14" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/lecture_07/mod_secant.m b/lecture_07/mod_secant.m index a3caa10..df97983 100644 --- a/lecture_07/mod_secant.m +++ b/lecture_07/mod_secant.m @@ -14,13 +14,13 @@ % ea = approximate relative error (%) % iter = number of iterations if nargin<3,error('at least 3 input arguments required'),end -if nargin<4 || isempty(es),es=0.0001;end +if nargin<4 || isempty(es),es=0.0001;end if nargin<5 || isempty(maxit),maxit=50;end -iter = 0; +iter = 0 while (1) xrold = xr; dfunc=(func(xr+dx)-func(xr))./dx; - xr = xr - func(xr)/dfunc; + xr = xr - func(xr)/dfunc; %calculate as an approximation iter = iter + 1; if xr ~= 0 ea = abs((xr - xrold)/xr) * 100; From 8fbcec30e8e79403adc4a68f061614f7b721ac9d Mon Sep 17 00:00:00 2001 From: Ryan M Hyatt Date: Tue, 21 Feb 2017 11:02:25 -0500 Subject: [PATCH 4/9] Lecture 10 Added instructions for merging, and commits to GaussNaive --- Pull.png | Bin 0 -> 47322 bytes lecture_10/GaussNaive.m | 4 +++- 2 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 Pull.png diff --git a/Pull.png b/Pull.png new file mode 100644 index 0000000000000000000000000000000000000000..d25c51281c46cc6f66880f344da40f27a7c20694 GIT binary patch literal 47322 zcmbrmbyOTp*DnfzAi*WLyTjlP!3LM$9vIx+CAe#FcXtN2;7$UAySuyR7*x#^(b3-%So1FmU!b7sL|p|bf1!1$*HW2@$6btUe28$nriHI#}~I 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From c0ba6e04d54a2ce89b48e434841f114193ae4562 Mon Sep 17 00:00:00 2001 From: Ryan M Hyatt Date: Sat, 25 Feb 2017 17:05:21 -0500 Subject: [PATCH 6/9] Revert "CRAP" This reverts commit 7d1ea3ba6f8a2c2dc038d82469338cbf672620bc. --- lecture_10/lecture_10.aux | 38 +++ lecture_10/lecture_10.log | 616 +++++++++++++++++++++++--------------- 2 files changed, 405 insertions(+), 249 deletions(-) diff --git a/lecture_10/lecture_10.aux b/lecture_10/lecture_10.aux index 154ec84..14a6f0d 100644 --- a/lecture_10/lecture_10.aux +++ b/lecture_10/lecture_10.aux @@ -1,2 +1,40 @@ \relax \providecommand\hyper@newdestlabel[2]{} +\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument} +\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined +\global\let\oldcontentsline\contentsline +\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}} +\global\let\oldnewlabel\newlabel +\gdef\newlabel#1#2{\newlabelxx{#1}#2} +\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}} +\AtEndDocument{\ifx\hyper@anchor\@undefined +\let\contentsline\oldcontentsline +\let\newlabel\oldnewlabel +\fi} +\fi} +\global\let\hyper@last\relax +\gdef\HyperFirstAtBeginDocument#1{#1} +\providecommand\HyField@AuxAddToFields[1]{} +\providecommand\HyField@AuxAddToCoFields[2]{} +\providecommand \oddpage@label [2]{} +\@writefile{toc}{\contentsline {section}{\numberline {1}Gauss Elimination}{1}{section.1}} +\newlabel{gauss-elimination}{{1}{1}{Gauss Elimination}{section.1}{}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {1.0.1}Solving sets of equations with matrix operations}{1}{subsubsection.1.0.1}} +\newlabel{solving-sets-of-equations-with-matrix-operations}{{1.0.1}{1}{Solving sets of equations with matrix operations}{subsubsection.1.0.1}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {1.1}Gauss elimination}{4}{subsection.1.1}} +\newlabel{gauss-elimination}{{1.1}{4}{Gauss elimination}{subsection.1.1}{}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {1.1.1}Solving sets of equations systematically}{4}{subsubsection.1.1.1}} +\newlabel{solving-sets-of-equations-systematically}{{1.1.1}{4}{Solving sets of equations systematically}{subsubsection.1.1.1}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Springs-masses\relax }}{4}{figure.caption.1}} +\@writefile{toc}{\contentsline {subsection}{\numberline {1.2}Automate Gauss Elimination}{6}{subsection.1.2}} +\newlabel{automate-gauss-elimination}{{1.2}{6}{Automate Gauss Elimination}{subsection.1.2}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {1.3}Problem (Diagonal element is zero)}{6}{subsection.1.3}} +\newlabel{problem-diagonal-element-is-zero}{{1.3}{6}{Problem (Diagonal element is zero)}{subsection.1.3}{}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {1.3.1}Spring-Mass System again}{8}{subsubsection.1.3.1}} +\newlabel{spring-mass-system-again}{{1.3.1}{8}{Spring-Mass System again}{subsubsection.1.3.1}{}} 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Package hyperref Info: Option `colorlinks' set `true' on input line 264. - (lecture_10.aux + (./lecture_10.aux LaTeX Warning: Label `gauss-elimination' multiply defined. @@ -628,7 +582,7 @@ LaTeX Font Info: ... okay on input line 271. LaTeX Font Info: Checking defaults for TS1/cmr/m/n on input line 271. LaTeX Font Info: Try loading font information for TS1+cmr on input line 271. - ("C:\Users\Ryan\AppData\Local\Programs\MiKTeX 2.9\tex\latex\base\ts1cmr.fd" + (/usr/share/texlive/texmf-dist/tex/latex/base/ts1cmr.fd File: ts1cmr.fd 2014/09/29 v2.5h Standard LaTeX font definitions ) LaTeX Font Info: ... okay on input line 271. @@ -636,11 +590,10 @@ LaTeX Font Info: Checking defaults for PD1/pdf/m/n on input line 271. LaTeX Font Info: ... okay on input line 271. 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LaTeX Error: Unknown graphics extension: .svg. + +See the LaTeX manual or LaTeX Companion for explanation. +Type H for immediate help. + ... + +l.712 ...egraphics{../lecture_09/mass_springs.svg} -l.1 ...MARK [1][-]{section.1}{Gauss Elimination}{} - % 1 -*** (job aborted, file error in nonstop mode) +? +[8] [9] +LaTeX Font Info: Font shape `T1/cmtt/bx/n' in size <10.95> not available +(Font) Font shape `T1/cmtt/m/n' tried instead on input line 812. + [10] + +File: lecture_10_files/lecture_10_27_0.pdf Graphic file (type pdf) - + +Package pdftex.def Info: lecture_10_files/lecture_10_27_0.pdf used on input lin +e 839. +(pdftex.def) Requested size: 449.6789pt x 337.25917pt. + +Underfull \hbox (badness 10000) in paragraph at lines 841--842 + + [] + +Package atveryend Info: Empty hook `BeforeClearDocument' on input line 852. +[11 <./lecture_10_files/lecture_10_27_0.pdf>] +Package atveryend Info: Empty hook `AfterLastShipout' on input line 852. + (./lecture_10.aux) +Package atveryend Info: Executing hook `AtVeryEndDocument' on input line 852. +Package atveryend Info: Executing hook `AtEndAfterFileList' on input line 852. +Package rerunfilecheck Info: File `lecture_10.out' has not changed. +(rerunfilecheck) Checksum: F41F49C8B1F4406369908EBDD24F2572;629. + + +LaTeX Warning: There were multiply-defined labels. + +Package atveryend Info: Empty hook `AtVeryVeryEnd' on input line 852. + ) Here is how much of TeX's memory you used: - 10704 strings out of 493333 - 162927 string characters out of 3130577 - 263754 words of memory out of 3000000 - 14067 multiletter control sequences out of 15000+200000 - 5746 words of font info for 18 fonts, out of 3000000 for 9000 + 10939 strings out of 493029 + 163764 string characters out of 6136234 + 273062 words of memory out of 5000000 + 14225 multiletter control sequences out of 15000+600000 + 35480 words of font info for 90 fonts, out of 8000000 for 9000 1141 hyphenation exceptions out of 8191 - 36i,1n,38p,389b,76s stack positions out of 5000i,500n,10000p,200000b,50000s -! ==> Fatal error occurred, no output PDF file produced! + 36i,8n,77p,494b,465s stack positions out of 5000i,500n,10000p,200000b,80000s +{/usr/share/texmf/fonts/enc/dvips/cm-super/cm-super-ts1.enc}{/usr/share/texli +ve/texmf-dist/fonts/enc/dvips/base/8r.enc}{/usr/share/texmf/fonts/enc/dvips/cm- +super/cm-super-t1.enc} +Output written on lecture_10.pdf (11 pages, 162693 bytes). +PDF statistics: + 165 PDF objects out of 1000 (max. 8388607) + 133 compressed objects within 2 object streams + 27 named destinations out of 1000 (max. 500000) + 80 words of extra memory for PDF output out of 10000 (max. 10000000) + From 710fdf7e323c3aeb9e14b6d7d551e6e4a6eec410 Mon Sep 17 00:00:00 2001 From: Ryan M Hyatt Date: Mon, 27 Mar 2017 15:32:07 -0400 Subject: [PATCH 7/9] committing test --- "lecture_15/\340\244\267\001" | 0 "lecture_15/\360&\341\001" | 0 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 "lecture_15/\340\244\267\001" delete mode 100644 "lecture_15/\360&\341\001" diff --git "a/lecture_15/\340\244\267\001" "b/lecture_15/\340\244\267\001" deleted file mode 100644 index e69de29..0000000 diff --git "a/lecture_15/\360&\341\001" "b/lecture_15/\360&\341\001" deleted file mode 100644 index e69de29..0000000 From 73286e0e6a40242b02297067c0fb2d435d429e2a Mon Sep 17 00:00:00 2001 From: Ryan M Hyatt Date: Thu, 13 Apr 2017 15:45:36 -0400 Subject: [PATCH 8/9] Some stuff blah --- HW5/README.md | 16 +- .../lecture_20-checkpoint.ipynb | 2196 ++- lecture_20/lecture_20.html | 13993 ++++++++++++++++ lecture_20/lecture_20.ipynb | 1 + lecture_20/lecture_20.pdf | Bin 0 -> 595332 bytes 5 files changed, 16196 insertions(+), 10 deletions(-) create mode 100644 lecture_20/lecture_20.html create mode 100644 lecture_20/lecture_20.pdf diff --git a/HW5/README.md b/HW5/README.md index a8495ea..953348b 100644 --- a/HW5/README.md +++ b/HW5/README.md @@ -4,7 +4,7 @@ *Include all work as either an m-file script, m-file function, or example code included with \`\`\` and document your code in the README.md file* -1. Create a new github repository called 'linear_algebra'. +1. Create a new github repository called 'linear_algebra'. a. Add rcc02007 and pez16103 as collaborators. @@ -13,7 +13,7 @@ with \`\`\` and document your code in the README.md file* 2. Create an LU-decomposition function called `lu_tridiag.m` that takes 3 vectors as inputs and calculates the LU-decomposition of a tridiagonal matrix. The output should be 3 vectors, the diagonal of the Upper matrix, and the two off-diagonal vectors of the Lower -and Upper matrices. +and Upper matrices. ```[ud,uo,lo]=lu_tridiag(e,f,g);``` @@ -26,7 +26,7 @@ the backslash solver `\`, create an algebraic solution* 4. Test your function on the matrices A3, A4, ..., A10 generated with `test_arrays.m` Solving for `b=ones(N,1);` where N is the size of A. In your `README.md` file, compare -the norm of the error between your result and the result of AN\b. +the norm of the error between your result and the result of AN\b. ``` | size of A | norm(error) | @@ -40,10 +40,10 @@ the norm of the error between your result and the result of AN\b. 5. In the system shown above, determine the three differential equations for the position of masses 1, 2, and 3. Solve for the vibrational modes of the spring-mass system if k1=10 N/m, k2=k3=20 N/m, and k4=10 N/m. The masses are m1=1 kg, m2=2 kg and m3=4 kg. Determine -the eigenvalues and natural frequencies. +the eigenvalues and natural frequencies. 6. The curvature of a slender column subject to an axial load P (Fig. P13.10) can be -modeled by +modeled by $\frac{d^{2}y}{dx^{2}} + p^{2} y = 0$ @@ -54,7 +54,7 @@ about its neutral axis. This model can be converted into an eigenvalue problem by substituting a centered finite-difference approximation for the second derivative to give -$\frac{y_{i+1} -2y_{i} + y_{i-1} }{\Delta x^{2}}+ p^{2} y_{i}$ +$\frac{y_{i+1} -2y_{i} + y_{i-1} }{\Delta x^{2}}+ p^{2} y_{i}$ where i = a node located at a position along the rod’s interior, and $\Delta x$ = the spacing between nodes. This equation can be expressed as $y_{i-1} - (2 - \Delta x^{2} @@ -66,11 +66,11 @@ Determine the eigenvalues for a 5-segment (4-interior nodes), 6-segment (5-inter nodes), and 10-segment (9-interior nodes). Using the modulus and moment of inertia of a pole for pole-vaulting ( [http://people.bath.ac.uk/taf21/sports_whole.htm](http://people.bath.ac.uk/taf21/sports_whole.htm)) -E=76E9 Pa, I=4E-8 m^4, and L= 5m. +E=76E9 Pa, I=4E-8 m^4, and L= 5m. Include a table in the `README.md` that shows the following results: What are the largest and smallest eigenvalues for the beam? How many eigenvalues are -there? +there? ``` | # of segments | largest | smallest | # of eigenvalues | diff --git a/lecture_20/.ipynb_checkpoints/lecture_20-checkpoint.ipynb b/lecture_20/.ipynb_checkpoints/lecture_20-checkpoint.ipynb index 2fd6442..0dbea1b 100644 --- a/lecture_20/.ipynb_checkpoints/lecture_20-checkpoint.ipynb +++ b/lecture_20/.ipynb_checkpoints/lecture_20-checkpoint.ipynb @@ -1,6 +1,2198 @@ { - "cells": [], - "metadata": {}, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "setdefaults" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%plot --format svg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Questions from last class\n", + "\n", + "The interp2 function uses splines to interpolate between data points. What are three options for interpolation:\n", + "\n", + "- cubic spline\n", + "\n", + "- piecewise cubic spline\n", + "\n", + "- linear spline\n", + "\n", + "- quadratic spline\n", + "\n", + "- fourth-order spline\n", + "\n", + "![q1](q1.png)\n", + "\n", + "Numerical integration is a general application of the Newton-Cotes formulas. What is the first order approximation of the Newton-Cotes formula? *\n", + "\n", + "- trapezoidal rule\n", + "\n", + "- Simpson's 1/3 rule\n", + "\n", + "- Simpson's 3/8 rule\n", + "\n", + "- linear approximation of integral\n", + "\n", + "- constant approximation of integral (sum(f(x)*dx))\n", + "\n", + "![q2](q2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Questions from you\n", + "\n", + "- Is spring here to stay?\n", + " \n", + " - [Punsxatawney Phil](http://www.groundhog.org/)\n", + "\n", + "- The time is now.\n", + "\n", + "- Final Project Sheet?\n", + " \n", + " - coming this evening/tomorrow\n", + "\n", + "- can you provide some sort of hw answer key?\n", + " \n", + " - we can go through some of the HW \n", + "\n", + "- What's the most 1337 thing you've ever done?\n", + "\n", + " - sorry, I'm n00b to this\n", + "\n", + "- Can we do out more examples by hand (doc cam or drawing on computer notepad) instead of with pre-written code?\n", + "\n", + " - forthcoming\n", + "\n", + "- Favorite movie?\n", + " \n", + " - Big Lebowski\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrals in practice\n", + "\n", + "### Example: Compare toughness of two steels\n", + "\n", + "![Stress-strain plot of steel](steel_psi.jpg)\n", + "\n", + "Use the plot shown to determine the toughness of stainless steel and the toughness of structural steel.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "toughness of structural steel is 10.2 psi\n", + "toughness of stainless steel is 18.6 psi\n" + ] + } + ], + "source": [ + "fe_c=load('structural_steel_psi.jpg.dat');\n", + "fe_cr =load('stainless_steel_psi.jpg.dat');\n", + "\n", + "fe_c_toughness=trapz(fe_c(:,1),fe_c(:,2));\n", + "fe_cr_toughness=trapz(fe_cr(:,1),fe_cr(:,2));\n", + "\n", + "fprintf('toughness of structural steel is %1.1f psi\\n',fe_c_toughness)\n", + "fprintf('toughness of stainless steel is %1.1f psi',fe_cr_toughness)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gauss Quadrature (for functions)\n", + "\n", + "Evaluating an integral, we assumed a polynomial form for each Newton-Cotes approximation.\n", + "\n", + "If we can evaluate the function at any point, it makes more sense to choose points more wisely rather than just using endpoints\n", + "\n", + "![trapezoidal example](trap_example.png)\n", + "\n", + "Let us set up two unknown constants, $c_{0}$ and $x_{0}$ and determine a *wise* place to evaluate f(x) such that \n", + "\n", + "$I=c_{0}f(x_{0})$\n", + "\n", + "and I is exact for polynomial of n=0, 1\n", + "\n", + "$\\int_{a}^{b}1dx=b-a=c_{0}$\n", + "\n", + "$\\int_{a}^{b}xdx=\\frac{b^2-a^2}{2}=c_{0}x_{0}$\n", + "\n", + "so $c_{0}=b-a$ and $x_{0}=\\frac{b+a}{2}$\n", + "\n", + "$I=\\int_{a}^{b}f(x)dx \\approx (b-a)f\\left(\\frac{b+a}{2}\\right)$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "f1 =\n", + "\n", + "@(x) x + 1\n", + "\n", + "f2 =\n", + "\n", + "@(x) 1 / 2 * x .^ 2 + x + 1\n", + "\n", + "f3 =\n", + "\n", + "@(x) 1 / 6 * x .^ 3 + 1 / 2 * x .^ 2 + x\n", + "\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1000\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1000\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-20\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f1=@(x) x+1\n", + "f2=@(x) 1/2*x.^2+x+1\n", + "f3=@(x) 1/6*x.^3+1/2*x.^2+x\n", + "plot(linspace(-18,18),f3(linspace(-18,18)))" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "integral of f1 from 2 to 3 = 3.500000\n", + "integral of f1 from 2 to 3 ~ 3.500000\n", + "integral of f2 from 2 to 3 = 6.666667\n", + "integral of f2 from 2 to 3 ~ 6.625000\n" + ] + } + ], + "source": [ + "fprintf('integral of f1 from 2 to 3 = %f',f2(3)-f2(2))\n", + "fprintf('integral of f1 from 2 to 3 ~ %f',(3-2)*f1(3/2+2/2))\n", + "\n", + "fprintf('integral of f2 from 2 to 3 = %f',f3(3)-f3(2))\n", + "fprintf('integral of f2 from 2 to 3 ~ %f',(3-2)*f2(3/2+2/2))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This process is called **Gauss Quadrature**. Usually, the bounds are fixed at -1 and 1 instead of a and b\n", + "\n", + "$I=c_{0}f(x_{0})$\n", + "\n", + "and I is exact for polynomial of n=0, 1\n", + "\n", + "$\\int_{-1}^{1}1dx=b-a=c_{0}$\n", + "\n", + "$\\int_{-1}^{1}xdx=\\frac{1^2-(-1)^2}{2}=c_{0}x_{0}$\n", + "\n", + "so $c_{0}=2$ and $x_{0}=0$\n", + "\n", + "$I=\\int_{-1}^{1}f(x)dx \\approx 2f\\left(0\\right)$\n", + "\n", + "Now, integrals can be performed with a change of variable\n", + "\n", + "a=2\n", + "\n", + "b=3\n", + "\n", + "x= 2 to 3\n", + "\n", + "or $x_{d}=$ -1 to 1\n", + "\n", + "$x=a_{1}+a_{2}x_{d}$\n", + "\n", + "at $x_{d}=-1$, x=a\n", + "\n", + "at $x_{d}=1$, x=b\n", + "\n", + "so \n", + "\n", + "$x=\\frac{(b+a) +(b-a)x_{d}}{2}$\n", + "\n", + "$dx=\\frac{b-a}{2}dx_{d}$\n", + "\n", + "$\\int_{2}^{3}x+1dx=\\int_{-1}^{1}\\left(\\frac{(2+3) +(3-2)x_{d}}{2}\n", + "+1\\right)\n", + "\\frac{3-2}{2}dx_{d}$\n", + "\n", + "$\\int_{2}^{3}x+1dx=\\int_{-1}^{1}\\left(\\frac{5 +x_{d}}{2}\n", + "+1\\right)\n", + "\\frac{3-2}{2}dx_{d}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\int_{2}^{3}x+1dx=\\int_{-1}^{1}\\left(\\frac{7}{4}+\\frac{1}{4}x_{d}\\right)dx_{d}=2\\frac{7}{4}=3.5$" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "function I=gauss_1pt(func,a,b)\n", + " % Gauss quadrature using single point\n", + " % exact for n<1 polynomials\n", + " c0=2;\n", + " xd=0;\n", + " dx=(b-a)/2;\n", + " x=(b+a)/2+(b-a)/2*xd;\n", + " I=func(x).*dx*c0;\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ans = 3.5000\r\n" + ] + } + ], + "source": [ + "gauss_1pt(f1,2,3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General Gauss weights and points\n", + "\n", + "![Gauss quadrature table](gauss_weights.png)\n", + "\n", + "### If you need to evaluate an integral, to increase accuracy, increase number of Gauss points\n", + "\n", + "### Adaptive Quadrature\n", + "\n", + "Matlab/Octave built-in functions use two types of adaptive quadrature to increase accuracy of integrals of functions. \n", + "\n", + "1. `quad`: Simpson quadrature good for nonsmooth functions\n", + "\n", + "2. `quadl`: Lobatto quadrature good for smooth functions\n", + "\n", + "```matlab\n", + "q = quad(fun, a, b, tol, trace, p1, p2, …)\n", + "fun : function to be integrates\n", + "a, b: integration bounds\n", + "tol: desired absolute tolerance (default: 10-6)\n", + "trace: flag to display details or not\n", + "p1, p2, …: extra parameters for fun\n", + "quadl has the same arguments\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ans = 6.6667\n", + "ans = 6.6667\n", + "ans = 6.6667\n" + ] + } + ], + "source": [ + "% integral of quadratic\n", + "quad(f2,2,3)\n", + "quadl(f2,2,3)\n", + "f3(3)-f3(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Numerical Differentiation\n", + "\n", + "Expanding the Taylor Series:\n", + "\n", + "$f(x_{i+1})=f(x_{i})+f'(x_{i})h+\\frac{f''(x_{i})}{2!}h^2+\\cdots$" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-4\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-3\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t3\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t4\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\t\n", + "\t\tLow noise in sin wave\n", + "\t\n", + "\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\tgnuplot_plot_2a\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x=linspace(-pi,pi);\n", + "y_smooth=sin(x);\n", + "y_noise =y_smooth+rand(size(x))*0.1-0.05;\n", + "plot(x,y_smooth,x,y_noise)\n", + "title('Low noise in sin wave')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-4\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-3\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t3\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t4\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\t\n", + "\t\tNoise Amplified with derivative\n", + "\t\n", + "\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\tgnuplot_plot_2a\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "% Central Difference derivative\n", + "\n", + "dy_smooth=zeros(size(x));\n", + "dy_smooth([1,end])=NaN;\n", + "dy_smooth(2:end-1)=(y_smooth(3:end)-y_smooth(1:end-2))/2/(x(2)-x(1));\n", + "\n", + "dy_noise=zeros(size(x));\n", + "dy_noise([1,end])=NaN;\n", + "dy_noise(2:end-1)=(y_noise(3:end)-y_noise(1:end-2))/2/(x(2)-x(1));\n", + "\n", + "plot(x,dy_smooth,x,dy_noise)\n", + "title('Noise Amplified with derivative')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reduce noise\n", + "\n", + "Options:\n", + "\n", + "1. Fit a function and take derivative\n", + " \n", + " a. splines won't help much\n", + " \n", + " b. best fit curve (better)\n", + " \n", + "2. Smooth data (does not matter if you smooth before/after derivative)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-4\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-3\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t3\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t4\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\tderiv of spline\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\tderiv of spline\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\tno change\n", + "\n", + "\t\n", + "\t\tno change\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_spline_i1=interp1(x,y_noise,x+0.1);\n", + "y_spline_in1=interp1(x,y_noise,x-0.1);\n", + "dy_spline=(y_spline_i1-y_spline_in1)/0.2;\n", + "plot(x,dy_spline,x,dy_noise)\n", + "legend('deriv of spline','no change')" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a = 0.99838\r\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1.5\n", + "\t\n", + "\n", + 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"metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a =\n", + "\n", + " 1 2 3 4 5\n", + "\n", + "pa =\n", + "\n", + " 2 1 1 2 3 4 5 5 4\n", + "\n", + "ans =\n", + "\n", + " 1 9\n", + "\n" + ] + } + ], + "source": [ + "a=[1,2,3,4,5]\n", + "pa=[a(4/2:-1:1) a a(end:-1:end-4/2+1)]\n", + "size(pa)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ans =\n", + "\n", + " 1 100\n", + "\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + 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"metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Octave", + "language": "octave", + "name": "octave" + }, + "language_info": { + "file_extension": ".m", + "help_links": [ + { + "text": "MetaKernel Magics", + "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" + } + ], + "mimetype": "text/x-octave", + "name": "octave", + "version": "0.19.14" + } + }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/lecture_20/lecture_20.html b/lecture_20/lecture_20.html new file mode 100644 index 0000000..5a7a4c6 --- /dev/null +++ b/lecture_20/lecture_20.html @@ -0,0 +1,13993 @@ + + + +lecture_20 + + + + + + + + + + + + + + + + + + + + + +
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Questions from last class

The interp2 function uses splines to interpolate between data points. What are three options for interpolation:

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  • cubic spline

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  • piecewise cubic spline

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  • linear spline

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  • quadratic spline

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  • fourth-order spline

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q1

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Numerical integration is a general application of the Newton-Cotes formulas. What is the first order approximation of the Newton-Cotes formula? *

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  • trapezoidal rule

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  • Simpson's 1/3 rule

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  • Simpson's 3/8 rule

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  • linear approximation of integral

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  • constant approximation of integral (sum(f(x)*dx))

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q2

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Questions from you

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  • Is spring here to stay?

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  • The time is now.

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  • Final Project Sheet?

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    • coming this evening/tomorrow
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  • can you provide some sort of hw answer key?

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    • we can go through some of the HW
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  • What's the most 1337 thing you've ever done?

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    • sorry, I'm n00b to this
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  • Can we do out more examples by hand (doc cam or drawing on computer notepad) instead of with pre-written code?

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    • forthcoming
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  • Favorite movie?

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    • Big Lebowski
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Integrals in practice

Example: Compare toughness of two steels

Stress-strain plot of steel

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Use the plot shown to determine the toughness of stainless steel and the toughness of structural steel.

+ +
+
+
+
+
+
In [49]:
+
+
+
fe_c=load('structural_steel_psi.jpg.dat');
+fe_cr =load('stainless_steel_psi.jpg.dat');
+
+fe_c_toughness=trapz(fe_c(:,1),fe_c(:,2));
+fe_cr_toughness=trapz(fe_cr(:,1),fe_cr(:,2));
+
+fprintf('toughness of structural steel is %1.1f psi\n',fe_c_toughness)
+fprintf('toughness of stainless steel is %1.1f psi',fe_cr_toughness)
+
+ +
+
+
+ +
+
+ + +
+
+
toughness of structural steel is 10.2 psi
+toughness of stainless steel is 18.6 psi
+
+
+
+ +
+
+ +
+
+
+
+
+
+

Gauss Quadrature (for functions)

Evaluating an integral, we assumed a polynomial form for each Newton-Cotes approximation.

+

If we can evaluate the function at any point, it makes more sense to choose points more wisely rather than just using endpoints

+

trapezoidal example

+

Let us set up two unknown constants, $c_{0}$ and $x_{0}$ and determine a wise place to evaluate f(x) such that

+

$I=c_{0}f(x_{0})$

+

and I is exact for polynomial of n=0, 1

+

$\int_{a}^{b}1dx=b-a=c_{0}$

+

$\int_{a}^{b}xdx=\frac{b^2-a^2}{2}=c_{0}x_{0}$

+

so $c_{0}=b-a$ and $x_{0}=\frac{b+a}{2}$

+

$I=\int_{a}^{b}f(x)dx \approx (b-a)f\left(\frac{b+a}{2}\right)$

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+
+
+
+
+
In [147]:
+
+
+
f1=@(x) x+1
+f2=@(x) 1/2*x.^2+x+1
+f3=@(x) 1/6*x.^3+1/2*x.^2+x
+plot(linspace(-18,18),f3(linspace(-18,18)))
+
+ +
+
+
+ +
+
+ + +
+
+
f1 =
+
+@(x) x + 1
+
+f2 =
+
+@(x) 1 / 2 * x .^ 2 + x + 1
+
+f3 =
+
+@(x) 1 / 6 * x .^ 3 + 1 / 2 * x .^ 2 + x
+
+
+
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+ +
+ +
+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1000 + + + + + -500 + + + + + 0 + + + + + 500 + + + + + 1000 + + + + + 1500 + + + + + -20 + + + + + -15 + + + + + -10 + + + + + -5 + + + + + 0 + + + + + 5 + + + + + 10 + + + + + 15 + + + + + 20 + + + + + + + + + gnuplot_plot_1a + + + + + + + + + + + + + + + +
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In [148]:
+
+
+
fprintf('integral of f1 from 2 to 3 = %f',f2(3)-f2(2))
+fprintf('integral of f1 from 2 to 3 ~ %f',(3-2)*f1(3/2+2/2))
+
+fprintf('integral of f2 from 2 to 3 = %f',f3(3)-f3(2))
+fprintf('integral of f2 from 2 to 3 ~ %f',(3-2)*f2(3/2+2/2))
+
+ +
+
+
+ +
+
+ + +
+
+
integral of f1 from 2 to 3 = 3.500000
+integral of f1 from 2 to 3 ~ 3.500000
+integral of f2 from 2 to 3 = 6.666667
+integral of f2 from 2 to 3 ~ 6.625000
+
+
+
+ +
+
+ +
+
+
+
+
+
+

This process is called Gauss Quadrature. Usually, the bounds are fixed at -1 and 1 instead of a and b

+

$I=c_{0}f(x_{0})$

+

and I is exact for polynomial of n=0, 1

+

$\int_{-1}^{1}1dx=b-a=c_{0}$

+

$\int_{-1}^{1}xdx=\frac{1^2-(-1)^2}{2}=c_{0}x_{0}$

+

so $c_{0}=2$ and $x_{0}=0$

+

$I=\int_{-1}^{1}f(x)dx \approx 2f\left(0\right)$

+

Now, integrals can be performed with a change of variable

+

a=2

+

b=3

+

x= 2 to 3

+

or $x_{d}=$ -1 to 1

+

$x=a_{1}+a_{2}x_{d}$

+

at $x_{d}=-1$, x=a

+

at $x_{d}=1$, x=b

+

so

+

$x=\frac{(b+a) +(b-a)x_{d}}{2}$

+

$dx=\frac{b-a}{2}dx_{d}$

+

$\int_{2}^{3}x+1dx=\int_{-1}^{1}\left(\frac{(2+3) +(3-2)x_{d}}{2} ++1\right) +\frac{3-2}{2}dx_{d}$

+

$\int_{2}^{3}x+1dx=\int_{-1}^{1}\left(\frac{5 +x_{d}}{2} ++1\right) +\frac{3-2}{2}dx_{d}$

+ +
+
+
+
+
+
+
+
+

$\int_{2}^{3}x+1dx=\int_{-1}^{1}\left(\frac{7}{4}+\frac{1}{4}x_{d}\right)dx_{d}=2\frac{7}{4}=3.5$

+ +
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+
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+
+
In [15]:
+
+
+
function I=gauss_1pt(func,a,b)
+    % Gauss quadrature using single point
+    % exact for n<1 polynomials
+    c0=2;
+    xd=0;
+    dx=(b-a)/2;
+    x=(b+a)/2+(b-a)/2*xd;
+    I=func(x).*dx*c0;
+end
+
+ +
+
+
+ +
+
+
+
In [13]:
+
+
+
gauss_1pt(f1,2,3)
+
+ +
+
+
+ +
+
+ + +
+
+
ans =  3.5000

+
+
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+ +
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+ +
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+
+

General Gauss weights and points

Gauss quadrature table

+

If you need to evaluate an integral, to increase accuracy, increase number of Gauss points

Adaptive Quadrature

Matlab/Octave built-in functions use two types of adaptive quadrature to increase accuracy of integrals of functions.

+
    +

  1. quad: Simpson quadrature good for nonsmooth functions

    +
    2. +

  2. quadl: Lobatto quadrature good for smooth functions

    +
    3. +
+
q = quad(fun, a, b, tol, trace, p1, p2, …)
+fun : function to be integrates
+a, b: integration bounds
+tol: desired absolute tolerance (default: 10-6)
+trace: flag to display details or not
+p1, p2, …: extra parameters for fun
+quadl has the same arguments
+
+ +
+
+
+
+
+
In [149]:
+
+
+
% integral of quadratic
+quad(f2,2,3)
+quadl(f2,2,3)
+f3(3)-f3(2)
+
+ +
+
+
+ +
+
+ + +
+
+
ans =  6.6667
+ans =  6.6667
+ans =  6.6667
+
+
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+ +
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+ +
+
+
+
+
+
+

Numerical Differentiation

Expanding the Taylor Series:

+

$f(x_{i+1})=f(x_{i})+f'(x_{i})h+\frac{f''(x_{i})}{2!}h^2+\cdots$

+ +
+
+
+
+
+
In [82]:
+
+
+
x=linspace(-pi,pi);
+y_smooth=sin(x);
+y_noise =y_smooth+rand(size(x))*0.1-0.05;
+plot(x,y_smooth,x,y_noise)
+title('Low noise in sin wave')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1.5 + + + + + -1 + + + + + -0.5 + + + + + 0 + + + + + 0.5 + + + + + 1 + + + + + 1.5 + + + + + -4 + + + + + -3 + + + + + -2 + + + + + -1 + + + + + 0 + + + + + 1 + + + + + 2 + + + + + 3 + + + + + 4 + + + + + + + + + Low noise in sin wave + + + + + gnuplot_plot_1a + + + + + + gnuplot_plot_2a + + + + + + + + + + + + + +
+ +
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In [98]:
+
+
+
% Central Difference derivative
+
+dy_smooth=zeros(size(x));
+dy_smooth([1,end])=NaN;
+dy_smooth(2:end-1)=(y_smooth(3:end)-y_smooth(1:end-2))/2/(x(2)-x(1));
+
+dy_noise=zeros(size(x));
+dy_noise([1,end])=NaN;
+dy_noise(2:end-1)=(y_noise(3:end)-y_noise(1:end-2))/2/(x(2)-x(1));
+
+plot(x,dy_smooth,x,dy_noise)
+title('Noise Amplified with derivative')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1.5 + + + + + -1 + + + + + -0.5 + + + + + 0 + + + + + 0.5 + + + + + 1 + + + + + 1.5 + + + + + -4 + + + + + -3 + + + + + -2 + + + + + -1 + + + + + 0 + + + + + 1 + + + + + 2 + + + + + 3 + + + + + 4 + + + + + + + + + Noise Amplified with derivative + + + + + gnuplot_plot_1a + + + + + + gnuplot_plot_2a + + + + + + + + + + + + + +
+ +
+ +
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+ +
+
+
+
+
+
+

Reduce noise

Options:

+
    +

  1. Fit a function and take derivative

    +

    a. splines won't help much

    +

    b. best fit curve (better)

    +
    2. +

  2. Smooth data (does not matter if you smooth before/after derivative)

    +
    3. +
+ +
+
+
+
+
+
In [99]:
+
+
+
y_spline_i1=interp1(x,y_noise,x+0.1);
+y_spline_in1=interp1(x,y_noise,x-0.1);
+dy_spline=(y_spline_i1-y_spline_in1)/0.2;
+plot(x,dy_spline,x,dy_noise)
+legend('deriv of spline','no change')
+
+ +
+
+
+ +
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+ + +
+ +
+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1.5 + + + + + -1 + + + + + -0.5 + + + + + 0 + + + + + 0.5 + + + + + 1 + + + + + 1.5 + + + + + -4 + + + + + -3 + + + + + -2 + + + + + -1 + + + + + 0 + + + + + 1 + + + + + 2 + + + + + 3 + + + + + 4 + + + + + + + + + + + + + deriv of spline + + + + + deriv of spline + + + + + + no change + + + no change + + + + + + + + + + + + + + + +
+ +
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+ +
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+
In [100]:
+
+
+
Z=[sin(x')];
+a=Z\y_noise'
+plot(x,a*sin(x),x,y_noise)
+
+ +
+
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+ +
+
+ + +
+
+
a =  0.99838

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+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1.5 + + + + + -1 + + + + + -0.5 + + + + + 0 + + + + + 0.5 + + + + + 1 + + + + + 1.5 + + + + + -4 + + + + + -3 + + + + + -2 + + + + + -1 + + + + + 0 + + + + + 1 + + + + + 2 + + + + + 3 + + + + + 4 + + + + + + + + + gnuplot_plot_1a + + + + + + gnuplot_plot_2a + + + + + + + + + + + + + +
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In [103]:
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plot(x,a*cos(x),x,dy_smooth,'o')
+
+ +
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+ +
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+ + +
+ +
+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1 + + + + + -0.5 + + + + + 0 + + + + + 0.5 + + + + + 1 + + + + + -4 + + + + + -3 + + + + + -2 + + + + + -1 + + + + + 0 + + + + + 1 + + + + + 2 + + + + + 3 + + + + + 4 + + + + + + + + + gnuplot_plot_1a + + + + + + gnuplot_plot_2a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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In [120]:
+
+
+
a=[1,2,3,4,5]
+pa=[a(4/2:-1:1) a a(end:-1:end-4/2+1)]
+size(pa)
+
+ +
+
+
+ +
+
+ + +
+
+
a =
+
+   1   2   3   4   5
+
+pa =
+
+   2   1   1   2   3   4   5   5   4
+
+ans =
+
+   1   9
+
+
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+ +
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+ +
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+
In [137]:
+
+
+
% Smooth data
+N=10; % average data between 10 points (forward/backward)
+y_data=[y_noise(N/2:-1:1) y_noise y_noise(end:-1:end-N/2+1)];
+y_filter=y_data;
+for i=6:length(x)
+    y_filter(i)=mean(y_data(i-5:i+5));
+end
+y_filter=y_filter(N/2:end-N/2-1);
+size(y_filter)
+plot(x,y_filter,x,y_noise,'.')
+
+ +
+
+
+ +
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+ + +
+
+
ans =
+
+     1   100
+
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+
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+ +
+ +
+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1.5 + + + + + -1 + + + + + -0.5 + + + + + 0 + + + + + 0.5 + + + + + 1 + + + + + 1.5 + + + + + -4 + + + + + -3 + + + + + -2 + + + + + -1 + + + + + 0 + + + + + 1 + + + + + 2 + + + + + 3 + + + + + 4 + + + + + + + + + gnuplot_plot_1a + + + + + + gnuplot_plot_2a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
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In [138]:
+
+
+
dy_filter=zeros(size(x));
+dy_filter([1,end])=NaN;
+dy_filter(2:end-1)=(y_filter(3:end)-y_filter(1:end-2))/2/(x(2)-x(1));
+
+plot(x,dy_smooth,x,dy_filter,x,dy_noise,'.')
+title('Noise Amplified with derivative')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +Gnuplot +Produced by GNUPLOT 5.0 patchlevel 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -1.5 + + + + + -1 + + + + + -0.5 + + + + + 0 + + + + + 0.5 + + + + + 1 + + + + + 1.5 + + + + + -4 + + + + + -3 + + + + + -2 + + + + + -1 + + + + + 0 + + + + + 1 + + + + + 2 + + + + + 3 + + + + + 4 + + + + + + + + + Noise Amplified with derivative + + + + + gnuplot_plot_1a + + + + + + gnuplot_plot_2a + + + + gnuplot_plot_3a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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In [ ]:
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+

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+ +
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+ + diff --git a/lecture_20/lecture_20.ipynb b/lecture_20/lecture_20.ipynb index da8c4ac..639f684 100644 --- a/lecture_20/lecture_20.ipynb +++ b/lecture_20/lecture_20.ipynb @@ -2169,6 +2169,7 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { "display_name": "Octave", "language": "octave", diff --git a/lecture_20/lecture_20.pdf b/lecture_20/lecture_20.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3b6d6ca5d0fab99c89b51948702ac79468456acf GIT binary patch literal 595332 zcmdSAXH=72w>FB3qJR_;5RhU4M5IaY57Gn#r1#!yD1iW>D2Pax-is7LAhb{uLXj@L z*MRgu2uLrXd_kY*efK`&J9~fooN<1fjEs?yd)@b1bIvu(wXT^kzm<{W;Nj#WWFA4U zFA(xDaWetT?Fq%ixTM{z%zywlrf13kA3H}!GcJBkZYINLI(E*M0FXPAiY5~eHz&6! z6YhclpD2@;0G}Znlaz~#qm_=8xsn}_i=S7JlUD%uh|+6K#ZBW(!;_ECxeEAof#KSR3;~W zLEOM^+3Wz$GG;(4re`uDFS&UHx%s#SczF4F`1shlxmj^<+ zU;bNjIXg!nZc#2dN8I!>Ru%wDD?+X}R?ap+TP9&1ZV3q{cOcFwzuo91+!>ogh z$^=H*IpCpv=lCHXQ205MVl0<^pdBf;>}c~nhnF{-@lCINS0J77K~ZsYxTL&G>wBpN z@%g%1qu}66FVzxeK{RJ}QK}2;yU-AN 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.../lecture_17-checkpoint.ipynb | 2709 ++++++++++++++++- lecture_17/lecture_17.ipynb | 1 + 2 files changed, 2708 insertions(+), 2 deletions(-) diff --git a/lecture_17/.ipynb_checkpoints/lecture_17-checkpoint.ipynb b/lecture_17/.ipynb_checkpoints/lecture_17-checkpoint.ipynb index 2fd6442..4d8aeeb 100644 --- a/lecture_17/.ipynb_checkpoints/lecture_17-checkpoint.ipynb +++ b/lecture_17/.ipynb_checkpoints/lecture_17-checkpoint.ipynb @@ -1,6 +1,2711 @@ { - "cells": [], - "metadata": {}, + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "setdefaults" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%plot --format svg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![question 1](q1.png)\n", + "\n", + "![question 2](q2.png)\n", + "\n", + "### Project Ideas so far\n", + "\n", + "- Nothing yet...probably something heat transfer related\n", + "\n", + "- Modeling Propulsion or Propagation of Sound Waves\n", + "\n", + "- Low Thrust Orbital Transfer\n", + "\n", + "- Tracking motion of a satellite entering orbit until impact\n", + "\n", + "- What ever you think is best.\n", + "\n", + "- You had heat transfer project as an option; that sounded cool\n", + "\n", + "- Heat transfer through a pipe\n", + "\n", + "- I would prefer to do something with beam/plate mechanics or vibrations than a heat transfer or thermo problem\n", + "\n", + "- Modeling Couette flow with a pressure gradient using a discretized form of the Navier-Stokes equation and comparing it to the analytical solution\n", + "\n", + "- Software to instruct a robotic arm to orient itself based on input from a gyroscope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Questions from you:\n", + "\n", + "How was your spring break\n", + "\n", + "Are grades for hw 3 and 4 going to be posted?\n", + "\n", + "For class occasionally switching to doc cam and working through problems by hand might help get ideas across.\n", + "\n", + "Are you assigning those who do not have groups to groups?\n", + "\n", + "How do you graph a standard distribution in Matlab?\n", + "\n", + "What is the longest code you have written?\n", + "\n", + "how to approach probability for hw 5?\n", + "??\n", + "\n", + "Will you be assigning groups to people who do not currently have one? \n", + "\n", + "what are some basic guidelines we should use to brainstorm project ideas?\n", + "\n", + "Are you a fan of bananas?\n", + "\n", + "Going through code isn't the most helpful, because you can easily lose interest. But I am not sure what else you can do.\n", + "\n", + "Has lecture 15 been posted yet? Looking in the repository I can't seem to find it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Least Squares Regression\n", + "\n", + "When approximating a set of data as a polynomial, there will always be error introduced (except in 2 cases). \n", + "\n", + "For a straight line, the actual data points, $y_{i}$ as a function of the independent variable, $x_{i}$, is:\n", + "\n", + "$y_{i}=a_{0}+a_{1}x_{i}+e_{i}$\n", + "\n", + "where $a_{0}$ and $a_{1}$ are the intercept and slope of the line and $e_{i}$ is the error between the approximate function and the recorded data point. \n", + "\n", + "We make the following assumptions in this analysis:\n", + "\n", + "1. Each x has a fixed value; it is not random and is known without error.\n", + "\n", + "2. The y values are independent random variables and all have the same variance.\n", + "\n", + "3. The y values for a given x must be normally distributed.\n", + "\n", + "The total error is \n", + "\n", + "$\\sum_{i=1}^{n}e_{i}=\\sum_{i=1}^{n}(y_{i}-a_{0}-a_{1}x_{i})$\n", + "\n", + "we don't care about the sign though. One approach has been demonstrated to provide a unique solution is minimizing the sum of squares error or\n", + "\n", + "$S_{r}=\\sum_{i=1}^{n}e_{i}^{2}=\\sum_{i=1}^{n}(y_{i}-a_{0}-a_{1}x_{i})^{2}$\n", + "\n", + "Where, $S_{r}$ is the sum of squares error (SSE). \n", + "\n", + "$\\frac{\\partial S_{r}}{\\partial a_{0}}=-2\\sum(y_{i}-a_{0}-a_{1}x_{i})$\n", + "\n", + "$\\frac{\\partial S_{r}}{\\partial a_{1}}=-2\\sum(y_{i}-a_{0}-a_{1}x_{i})x_{i}$\n", + "\n", + "The minimum $S_{r}$ occurrs when the partial derivatives are 0. \n", + "\n", + "$\\sum y_{i}= \\sum a_{0}+\\sum a_{1}x_{i}$\n", + "\n", + "$\\sum x_{i}y_{i}= \\sum a_{0}x_{i}+\\sum a_{1}x_{i}^{2}$\n", + "\n", + "$\\left[\\begin{array}{c}\n", + "\\sum y_{i}\\\\\n", + "\\sum x_{i}y_{i}\\end{array}\\right]=\n", + "\\left[\\begin{array}{cc}\n", + "n & \\sum x_{i}\\\\\n", + "\\sum x_{i} & \\sum x_{i}^{2}\\end{array}\\right]\n", + "\\left[\\begin{array}{c}\n", + "a_{0}\\\\\n", + "a_{1}\\end{array}\\right]$\n", + "\n", + "\n", + "$b=Ax$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example \n", + "\n", + "Find drag coefficient with best-fit line to experimental data\n", + "\n", + "|i | v (m/s) | F (N) |\n", + "|---|---|---|\n", + "|1 | 10 | 25 |\n", + "|2 | 20 | 70 |\n", + "|3 | 30 | 380|\n", + "|4 | 40 | 550|\n", + "|5 | 50 | 610|\n", + "|6 | 60 | 1220|\n", + "|7 | 70 | 830 |\n", + "|8 |80 | 1450|" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a =\n", + "\n", + " -234.286\n", + " 19.470\n", + "\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1000\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t2000\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t30\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t40\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t50\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t60\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t70\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t80\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\t\n", + "\t\tvelocity (m/s)\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\tForce (N)\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\tdata\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\tdata\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "\t\n", + "\tbest-fit\n", + "\n", + "\t\n", + "\t\tbest-fit\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "drag_data=[...\n", + "1 , 10 , 25 \n", + "2 , 20 , 70 \n", + "3 , 30 , 380\n", + "4 , 40 , 550\n", + "5 , 50 , 610\n", + "6 , 60 , 1220\n", + "7 , 70 , 830 \n", + "8 ,80 , 1450];\n", + "x=drag_data(:,2);\n", + "y=drag_data(:,3);\n", + "\n", + "b=[sum(y);sum(x.*y)];\n", + "A=[length(x),sum(x);\n", + " sum(x), sum(x.^2)];\n", + " \n", + "a=A\\b\n", + "\n", + "plot(x,y,'o',x,a(1)+a(2)*x)\n", + "legend('data','best-fit','Location','NorthWest')\n", + "xlabel('Force (N)')\n", + "ylabel('velocity (m/s)')" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-2000\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1000\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-500\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t30\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t40\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t50\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t60\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t70\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t80\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\t\n", + "\t\tresiduals (N)\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\tvelocity (m/s)\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\tModel does not capture measurements\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\tdata\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\tdata\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(x,y-a(1)-40*x)\n", + "legend('data','best-fit','Location','NorthWest')\n", + "ylabel('residuals (N)')\n", + "xlabel('velocity (m/s)')\n", + "title('Model does not capture measurements')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How do we know its a \"good\" fit? \n", + "\n", + "Can compare the sum of squares error to the total sum of squares of the dependent variable (here F). \n", + "\n", + "$S_{r}=\\sum(y_{i}-a_{0}-a_{1}x_{i})^{2}$\n", + "\n", + "$S_{t}=\\sum(y_{i}-\\bar{y})^{2}$\n", + "\n", + "Then, we can calculate the *coefficient of determination*, $r^{2}$ or *correlation coefficient*, r. \n", + "\n", + "$r^{2}=\\frac{S_{t}-S_{r}}{S_{t}}$\n", + "\n", + "This represents the relative improvement of assuming that y is a function of x (if the x-values are not random and the y-values are random)\n", + "\n", + "For further information regarding statistical work on regression, look at \n", + "[NIST Statistics Handbook](http://www.itl.nist.gov/div898/handbook/pmd/section4/pmd44.htm)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "St = 1.8083e+06\n", + "St = 1.8083e+06\n" + ] + } + ], + "source": [ + "Sr=sum((y-a(1)-a(2)*x).^2);\n", + "St=std(y)^2*(length(y)-1)\n", + "St=sum((y-mean(y)).^2)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 = 0.88049\n", + "r = 0.93834\n" + ] + } + ], + "source": [ + "r2=(St-Sr)/St\n", + "r=sqrt(r2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Limiting cases \n", + "\n", + "#### $r^{2}=0$ $S_{r}=S{t}$\n", + "\n", + "#### $r^{2}=1$ $S_{r}=0$" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.4\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.6\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.8\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.4\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.6\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.8\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\tCase 1\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\tCase 1\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "\t\n", + "\tCase 2\n", + "\n", + "\t\n", + "\t\tCase 2\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_examples\n", + "plot(x1(1:50:end),y1(1:50:end),'s',x2,y2,'o')\n", + "legend('Case 1','Case 2','Location','NorthWest')" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a1 =\n", + "\n", + " 0.0482496\n", + " 0.0017062\n", + "\n", + "a2 =\n", + "\n", + " 0\n", + " 1\n", + "\n", + "Sr1 = 0.83505\n", + "St1 = 0.83529\n", + "coefficient of determination in Case 1 is 0.000291\n", + "Sr2 = 0\n", + "St2 = 1.0185\n", + "coefficient of determination in Case 2 is 1.000000\n" + ] + } + ], + "source": [ + "b1=[sum(y1);sum(x1.*y1)];\n", + "A1=[length(x1),sum(x1);\n", + " sum(x1), sum(x1.^2)];\n", + " \n", + "a1=A1\\b1\n", + "\n", + "b2=[sum(y2);sum(x2.*y2)];\n", + "A2=[length(x2),sum(x2);\n", + " sum(x2), sum(x2.^2)];\n", + " \n", + "a2=A2\\b2\n", + "\n", + "Sr1=sum((y1-a1(1)-a1(2)*x1).^2)\n", + "St1=sum((y1-mean(y1)).^2)\n", + "fprintf('coefficient of determination in Case 1 is %f\\n',1-Sr1/St1)\n", + "\n", + "Sr2=sum((y2-a2(1)-a2(2)*x2).^2)\n", + "St2=sum((y2-mean(y2)).^2)\n", + "\n", + "fprintf('coefficient of determination in Case 2 is %f\\n',1-Sr2/St2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# General Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, you may want to fit other polynomials besides degree-1 (straight-lines)\n", + "\n", + "$y=a_{0}+a_{1}x+a_{2}x^{2}+\\cdots+a_{m}x^{m}+e$\n", + "\n", + "Now, the solution for $a_{0},~a_{1},...a_{m}$ is the minimization of m+1-dependent linear equations. \n", + "\n", + "Consider the following data:\n", + "\n", + "| x | y |\n", + "|---|---|\n", + "| 0.00 | 21.50 |\n", + "| 2.00 | 20.84 |\n", + "| 4.00 | 23.19 |\n", + "| 6.00 | 22.69 |\n", + "| 8.00 | 30.27 |\n", + "| 10.00 | 40.11 |\n", + "| 12.00 | 43.31 |\n", + "| 14.00 | 54.79 |\n", + "| 16.00 | 70.88 |\n", + "| 18.00 | 89.48 |\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t30\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t40\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t50\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t60\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t70\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t80\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t90\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t100\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t \n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load xy_data.csv\n", + "x=xy_data(1,:)';\n", + "y=xy_data(2,:)';\n", + "plot(x,y,'o')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xy_data =\n", + "\n", + " Columns 1 through 7:\n", + "\n", + " 0.00000 2.00000 4.00000 6.00000 8.00000 10.00000 12.00000\n", + " 21.50114 20.84153 23.19201 22.69498 30.26687 40.11075 43.30543\n", + "\n", + " Columns 8 through 11:\n", + "\n", + " 14.00000 16.00000 18.00000 20.00000\n", + " 54.78730 70.88443 89.48368 97.28135\n", + "\n" + ] + } + ], + "source": [ + "xy_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model can be rewritten as \n", + "\n", + "$y=\\left[Z\\right]a+e$\n", + "\n", + "where $a=\\left[\\begin{array}{c}\n", + " a_{0}\\\\\n", + " a_{1}\\\\\n", + " a_{2}\\end{array}\\right]$\n", + " \n", + "$[Z]=\\left[\\begin{array} \n", + "1 & x_{1} & x_{1}^{2} \\\\\n", + "1 & x_{2} & x_{2}^{2} \\\\\n", + "1 & x_{3} & x_{3}^{2} \\\\\n", + "1 & x_{4} & x_{4}^{2} \\\\\n", + "1 & x_{5} & x_{5}^{2} \\\\\n", + "1 & x_{6} & x_{6}^{2} \\\\\n", + "1 & x_{7} & x_{7}^{2} \\\\\n", + "1 & x_{8} & x_{8}^{2} \\\\\n", + "1 & x_{9} & x_{9}^{2} \\\\\n", + "1 & x_{10} & x_{10}^{2} \\end{array}\\right]$\n", + "\n", + "The sum of squares residuals for this model is\n", + "\n", + "$S_{r}=\\sum_{i=1}^{n}\\left(y_{i}-\\sum_{j=0}^{m}a_{j}z_{ji}\\right)$\n", + "\n", + "Minimizing this function results in\n", + "\n", + "$y=[Z]a$\n", + "\n", + "->**A standard Linear Algebra Problem**\n", + "\n", + "*the vector a is unknown, and Z is calculated based upon the assumed function*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z =\n", + "\n", + " 1 0 0\n", + " 1 2 4\n", + " 1 4 16\n", + " 1 6 36\n", + " 1 8 64\n", + " 1 10 100\n", + " 1 12 144\n", + " 1 14 196\n", + " 1 16 256\n", + " 1 18 324\n", + " 1 20 400\n", + "\n", + "a =\n", + "\n", + " 21.40341\n", + " -0.81538\n", + " 0.23935\n", + "\n" + ] + } + ], + "source": [ + "Z=[x.^0,x,x.^2]\n", + "\n", + "a=Z\\y" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t40\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t60\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t80\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t100\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t120\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t \n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "\t\n", + "\tgnuplot_plot_2a\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_fcn=linspace(min(x),max(x));\n", + "plot(x,y,'o',x_fcn,a(1)+a(2)*x_fcn+a(3)*x_fcn.^2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General Coefficient of Determination\n", + "\n", + "$r^{2}=\\frac{S_{t}-S_{r}}{S_{t}}=1-\\frac{S_{r}}{S_t}$" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "St = 27.923\n", + "Sr = 2.6366\n" + ] + } + ], + "source": [ + "St=std(y)\n", + "Sr=std(y-a(1)-a(2)*x-a(3)*x.^2)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the coefficient of determination for this fit is 0.880485\n", + "the correlation coefficient this fit is 0.938342\n" + ] + } + ], + "source": [ + "r2=1-Sr/St;\n", + "r=sqrt(r2);\n", + "\n", + "fprintf('the coefficient of determination for this fit is %f',r2)\n", + "fprintf('the correlation coefficient this fit is %f',r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare this to a straight line fit" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "St = 27.923\n", + "Sr = 9.2520\n", + "the coefficient of determination for this fit is 0.668655\n", + "the correlation coefficient this fit is 0.817713\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t40\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t60\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t80\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t100\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t120\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t \n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "\t\n", + "\tgnuplot_plot_2a\n", + "\n", + "\t\n", + "\t\n", + "\tgnuplot_plot_3a\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Z=[ones(size(x)) x];\n", + "a_line=Z\\y;\n", + "x_fcn=linspace(min(x),max(x));\n", + "plot(x,y,'o',x_fcn,a(1)+a(2)*x_fcn+a(3)*x_fcn.^2,...\n", + "x_fcn,a_line(1)+a_line(2)*x_fcn)\n", + "St=std(y)\n", + "Sr=std(y-a_line(1)-a_line(2)*x)\n", + "r2=1-Sr/St;\n", + "r=sqrt(r2);\n", + "\n", + "fprintf('the coefficient of determination for this fit is %f',r2)\n", + "fprintf('the correlation coefficient this fit is %f',r)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\t\n", + "\t\tresiduals of straight line\n", + "\t\n", + "\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(x,y-a_line(1)-a_line(2)*x)\n", + "title('residuals of straight line')" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-4\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t2\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t4\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t6\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\t\n", + "\t\tresiduals of parabola\n", + "\t\n", + "\n", + "\n", + "\n", + "\tgnuplot_plot_1a\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(x,y-a(1)-a(2)*x-a(3)*x.^2)\n", + "title('residuals of parabola')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Warning \n", + "**Coefficient of determination reduction does not always mean a better fit**\n", + "\n", + "Try the function, \n", + "\n", + "$y(x)=a_0+a_{1}x+a_{2}x^{2}+a_{4}x^{4}+a_{5}x^{5}+a_{5}x^{5}+a_{6}x^{6}+a_{7}x^{7}+a_{8}x^{8}$" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a_overfit =\n", + "\n", + " 2.1487e+01\n", + " -1.4264e+01\n", + " 1.5240e+01\n", + " -6.0483e+00\n", + " 1.1887e+00\n", + " -1.2651e-01\n", + " 7.4379e-03\n", + " -2.2702e-04\n", + " 2.8063e-06\n", + "\n" + ] + } + ], + "source": [ + "Z=[ones(size(x)) x x.^2 x.^3 x.^4 x.^5 x.^6 x.^7 x.^8];\n", + "a_overfit=Z\\y" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t40\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t60\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t80\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t100\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t120\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t10\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t15\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t20\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\tdata\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\tdata\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "\t\n", + "\tparabola\n", + "\n", + "\t\n", + "\t\tparabola\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\tn=8-fit\n", + "\n", + "\t\n", + "\t\tn=8-fit\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(x,y,'o',x_fcn,a(1)+a(2)*x_fcn+a(3)*x_fcn.^2,...\n", + "x_fcn,a_overfit(1)+a_overfit(2)*x_fcn+a_overfit(3)*x_fcn.^2+...\n", + "a_overfit(4)*x_fcn.^3+a_overfit(5)*x_fcn.^4+...\n", + "a_overfit(6)*x_fcn.^5+a_overfit(7)*x_fcn.^6+...\n", + "a_overfit(8)*x_fcn.^7+a_overfit(9)*x_fcn.^8)\n", + "legend('data','parabola','n=8-fit','Location','NorthWest')" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "St = 27.923\n", + "Sr = 0.77320\n", + "the coefficient of determination for this fit is 0.972309\n", + "the correlation coefficient this fit is 0.986057\n" + ] + } + ], + "source": [ + "St=std(y)\n", + "Sr=std(y-polyval(a_overfit(end:-1:1),x))\n", + "r2=1-Sr/St;\n", + "r=sqrt(r2);\n", + "\n", + "fprintf('the coefficient of determination for this fit is %f',r2)\n", + "fprintf('the correlation coefficient this fit is %f',r)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear Regression is only limited by the ability to separate the parameters from the function to achieve\n", + "\n", + "$y=[Z]a$\n", + "\n", + "$Z$ can be any function of the independent variable(s)\n", + "\n", + "**Example**:\n", + "We assume two functions are added together, sin(t) and sin(3t). What are the amplitudes?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t1.5\n", + 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[ + "a =\n", + "\n", + " 0.99727\n", + " 0.50251\n", + "\n" + ] + } + ], + "source": [ + "Z=[sin(t) sin(3*t)];\n", + "a=Z\\y" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "Gnuplot\n", + "Produced by GNUPLOT 5.0 patchlevel 3 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\t\n", + "\t \n", + "\t \n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\n", + "\n", + "\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-1\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t-0.5\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0\n", + "\t\n", + "\n", + "\n", + "\t\t\n", + "\t\t0.5\n", + "\t\n", + "\n", 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"metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Octave", + "language": "octave", + "name": "octave" + }, + "language_info": { + "file_extension": ".m", + "help_links": [ + { + "text": "MetaKernel Magics", + "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" + } + ], + "mimetype": "text/x-octave", + "name": "octave", + "version": "0.19.14" + } + }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/lecture_17/lecture_17.ipynb b/lecture_17/lecture_17.ipynb index 35e61da..4d8aeeb 100644 --- a/lecture_17/lecture_17.ipynb +++ b/lecture_17/lecture_17.ipynb @@ -2687,6 +2687,7 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { "display_name": "Octave", "language": "octave",

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