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< a href = "#nested-classes" > Data Structures< / a > |
< a href = "#define-members" > Macros< / a > |
< a href = "#func-members" > Functions< / a > < / div >
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< div class = "title" > adaline_learning.c File Reference< / div > < / div >
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< p > < a href = "https://en.wikipedia.org/wiki/ADALINE" > Adaptive Linear Neuron (ADALINE)< / a > implementation
< a href = "#details" > More...< / a > < / p >
< div class = "textblock" > < code > #include < assert.h> < / code > < br / >
< code > #include < limits.h> < / code > < br / >
< code > #include < math.h> < / code > < br / >
< code > #include < stdbool.h> < / code > < br / >
< code > #include < stdio.h> < / code > < br / >
< code > #include < stdlib.h> < / code > < br / >
< code > #include < time.h> < / code > < br / >
< / div > < div class = "textblock" > < div class = "dynheader" >
Include dependency graph for adaline_learning.c:< / div >
< div class = "dyncontent" >
< div class = "center" > < iframe scrolling = "no" frameborder = "0" src = "../../d9/dcc/adaline__learning_8c__incl.svg" width = "611" height = "127" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
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< / div > < table class = "memberdecls" >
< tr class = "heading" > < td colspan = "2" > < h2 class = "groupheader" > < a name = "nested-classes" > < / a >
Data Structures< / h2 > < / td > < / tr >
< tr class = "memitem:" > < td class = "memItemLeft" align = "right" valign = "top" > struct   < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > < / td > < / tr >
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< tr class = "memdesc:" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > structure to hold adaline model parameters < a href = "../../d2/daa/structadaline.html#details" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
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Macros< / h2 > < / td > < / tr >
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#define  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#acd517c6f195c75b9dd0f3aad65326f3b" > MAX_ITER< / a >       500< / td > < / tr >
< tr class = "memdesc:acd517c6f195c75b9dd0f3aad65326f3b" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Maximum number of iterations to learn. < br / > < / td > < / tr >
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< tr class = "memdesc:af270a96662132d0385cb6b4637c5a689" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > convergence accuracy \(=1\times10^{-5}\) < br / > < / td > < / tr >
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Functions< / h2 > < / td > < / tr >
< tr class = "memitem:acd88962c5f6341e43cbc69b4a7d3485b" > < td class = "memItemLeft" align = "right" valign = "top" > struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a >   < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#acd88962c5f6341e43cbc69b4a7d3485b" > new_adaline< / a > (const int num_features, const double eta)< / td > < / tr >
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< tr class = "memdesc:acd88962c5f6341e43cbc69b4a7d3485b" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Default constructor. < a href = "../../dd/d8c/adaline__learning_8c.html#acd88962c5f6341e43cbc69b4a7d3485b" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:acd88962c5f6341e43cbc69b4a7d3485b" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:a6f35caa3084772cc126ac7b20f67f665" > < td class = "memItemLeft" align = "right" valign = "top" > void  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a6f35caa3084772cc126ac7b20f67f665" > delete_adaline< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada)< / td > < / tr >
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< tr class = "memdesc:a6f35caa3084772cc126ac7b20f67f665" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > delete dynamically allocated memory < a href = "../../dd/d8c/adaline__learning_8c.html#a6f35caa3084772cc126ac7b20f67f665" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:a6f35caa3084772cc126ac7b20f67f665" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
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int  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a7b12b6513314c975303b5a698608322f" > activation< / a > (double x)< / td > < / tr >
< tr class = "memdesc:a7b12b6513314c975303b5a698608322f" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > < a href = "https://en.wikipedia.org/wiki/Heaviside_step_function" > Heaviside activation function< / a > < img src = "https://upload.wikimedia.org/wikipedia/commons/d/d9/Dirac_distribution_CDF.svg" alt = "" style = "pointer-events: none;" width = "200px" class = "inline" / > < br / > < / td > < / tr >
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char *  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada)< / td > < / tr >
< tr class = "memdesc:a00dc6134ca22a12e0fd9cac54d601c2d" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Operator to print the weights of the model. < br / > < / td > < / tr >
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< tr class = "separator:a00dc6134ca22a12e0fd9cac54d601c2d" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:a4db1ba00a7f282100ea31a94e32bd7a3" > < td class = "memItemLeft" align = "right" valign = "top" > int  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > predict< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada, const double *x, double *out)< / td > < / tr >
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< tr class = "memdesc:a4db1ba00a7f282100ea31a94e32bd7a3" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > predict the output of the model for given set of features < a href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:a4db1ba00a7f282100ea31a94e32bd7a3" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:afa953e811eaae199d121a7ddb619d604" > < td class = "memItemLeft" align = "right" valign = "top" > double  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#afa953e811eaae199d121a7ddb619d604" > fit_sample< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada, const double *x, const int y)< / td > < / tr >
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< tr class = "memdesc:afa953e811eaae199d121a7ddb619d604" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Update the weights of the model using supervised learning for one feature vector. < a href = "../../dd/d8c/adaline__learning_8c.html#afa953e811eaae199d121a7ddb619d604" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:afa953e811eaae199d121a7ddb619d604" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:a355fda53c238a0c0c07f03dcc021caed" > < td class = "memItemLeft" align = "right" valign = "top" > void  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a355fda53c238a0c0c07f03dcc021caed" > fit< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada, double **X, const int *y, const int < a class = "el" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > )< / td > < / tr >
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< tr class = "memdesc:a355fda53c238a0c0c07f03dcc021caed" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Update the weights of the model using supervised learning for an array of vectors. < a href = "../../dd/d8c/adaline__learning_8c.html#a355fda53c238a0c0c07f03dcc021caed" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:a355fda53c238a0c0c07f03dcc021caed" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:ab4ecb3accf5d9e0263087e7265bbe3a9" > < td class = "memItemLeft" align = "right" valign = "top" > void  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#ab4ecb3accf5d9e0263087e7265bbe3a9" > test1< / a > (double eta)< / td > < / tr >
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< tr class = "memdesc:ab4ecb3accf5d9e0263087e7265bbe3a9" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > test function to predict points in a 2D coordinate system above the line \(x=y\) as +1 and others as -1. < a href = "../../dd/d8c/adaline__learning_8c.html#ab4ecb3accf5d9e0263087e7265bbe3a9" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:ab4ecb3accf5d9e0263087e7265bbe3a9" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:a05cc9a0acb524fde727a4d7b4a747ee6" > < td class = "memItemLeft" align = "right" valign = "top" > void  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a05cc9a0acb524fde727a4d7b4a747ee6" > test2< / a > (double eta)< / td > < / tr >
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< tr class = "memdesc:a05cc9a0acb524fde727a4d7b4a747ee6" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > test function to predict points in a 2D coordinate system above the line \(x+3y=-1\) as +1 and others as -1. < a href = "../../dd/d8c/adaline__learning_8c.html#a05cc9a0acb524fde727a4d7b4a747ee6" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:a05cc9a0acb524fde727a4d7b4a747ee6" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:a3f37b9f073f7e57fd0b39d70718af1b1" > < td class = "memItemLeft" align = "right" valign = "top" > void  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a3f37b9f073f7e57fd0b39d70718af1b1" > test3< / a > (double eta)< / td > < / tr >
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< tr class = "memdesc:a3f37b9f073f7e57fd0b39d70718af1b1" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > test function to predict points in a 3D coordinate system lying within the sphere of radius 1 and centre at origin as +1 and others as -1. < a href = "../../dd/d8c/adaline__learning_8c.html#a3f37b9f073f7e57fd0b39d70718af1b1" > More...< / a > < br / > < / td > < / tr >
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< tr class = "separator:a3f37b9f073f7e57fd0b39d70718af1b1" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
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< tr class = "memitem:a3c04138a5bfe5d72780bb7e82a18e627" > < td class = "memItemLeft" align = "right" valign = "top" > < a id = "a3c04138a5bfe5d72780bb7e82a18e627" > < / a >
int  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../dd/d8c/adaline__learning_8c.html#a3c04138a5bfe5d72780bb7e82a18e627" > main< / a > (int argc, char **argv)< / td > < / tr >
< tr class = "memdesc:a3c04138a5bfe5d72780bb7e82a18e627" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Main function. < br / > < / td > < / tr >
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< tr class = "separator:a3c04138a5bfe5d72780bb7e82a18e627" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< / table >
< a name = "details" id = "details" > < / a > < h2 class = "groupheader" > Detailed Description< / h2 >
< div class = "textblock" > < p > < a href = "https://en.wikipedia.org/wiki/ADALINE" > Adaptive Linear Neuron (ADALINE)< / a > implementation < / p >
< dl class = "section author" > < dt > Author< / dt > < dd > < a href = "https://github.com/kvedala" > Krishna Vedala< / a > < / dd > < / dl >
< p > < img src = "https://upload.wikimedia.org/wikipedia/commons/b/be/Adaline_flow_chart.gif" alt = "" width = "200px" class = "inline" / > < a href = "https://commons.wikimedia.org/wiki/File:Adaline_flow_chart.gif" > source< / a > ADALINE is one of the first and simplest single layer artificial neural network. The algorithm essentially implements a linear function < / p > < p class = "formulaDsp" >
\[ f\left(x_0,x_1,x_2,\ldots\right) = \sum_j x_jw_j+\theta \]
< / p >
< p > where \(x_j\) are the input features of a sample, \(w_j\) are the coefficients of the linear function and \(\theta\) is a constant. If we know the \(w_j\), then for any given set of features, \(y\) can be computed. Computing the \(w_j\) is a supervised learning algorithm wherein a set of features and their corresponding outputs are given and weights are computed using stochastic gradient descent method. < / p >
< / div > < h2 class = "groupheader" > Function Documentation< / h2 >
< a id = "a6f35caa3084772cc126ac7b20f67f665" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#a6f35caa3084772cc126ac7b20f67f665" > ◆ < / a > < / span > delete_adaline()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
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< td class = "memname" > void delete_adaline < / td >
< td > (< / td >
< td class = "paramtype" > struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *  < / td >
< td class = "paramname" > < em > ada< / em > < / td > < td > )< / td >
< td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > delete dynamically allocated memory < / p >
< dl class = "params" > < dt > Parameters< / dt > < dd >
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< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > ada< / td > < td > model from which the memory is to be freeed. < / td > < / tr >
< / table >
< / dd >
< / dl >
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< div class = "fragment" > < div class = "line" > < a name = "l00083" > < / a > < span class = "lineno" > 83< / span >   {< / div >
< div class = "line" > < a name = "l00084" > < / a > < span class = "lineno" > 84< / span >   < span class = "keywordflow" > if< / span > (ada == NULL)< / div >
< div class = "line" > < a name = "l00085" > < / a > < span class = "lineno" > 85< / span >   < span class = "keywordflow" > return< / span > ;< / div >
< div class = "line" > < a name = "l00086" > < / a > < span class = "lineno" > 86< / span >   < / div >
< div class = "line" > < a name = "l00087" > < / a > < span class = "lineno" > 87< / span >   free(ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > );< / div >
< div class = "line" > < a name = "l00088" > < / a > < span class = "lineno" > 88< / span >   };< / div >
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< / div > <!-- fragment -->
< / div >
< / div >
< a id = "a355fda53c238a0c0c07f03dcc021caed" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#a355fda53c238a0c0c07f03dcc021caed" > ◆ < / a > < / span > fit()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > void fit < / td >
< td > (< / td >
< td class = "paramtype" > struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *  < / td >
< td class = "paramname" > < em > ada< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > double **  < / td >
< td class = "paramname" > < em > X< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > const int *  < / td >
< td class = "paramname" > < em > y< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > const int  < / td >
< td class = "paramname" > < em > N< / em >   < / td >
< / tr >
< tr >
< td > < / td >
< td > )< / td >
< td > < / td > < td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > Update the weights of the model using supervised learning for an array of vectors. < / p >
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< dl class = "params" > < dt > Parameters< / dt > < dd >
< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > ada< / td > < td > adaline model to train < / td > < / tr >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > X< / td > < td > array of feature vector < / td > < / tr >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > y< / td > < td > known output value for each feature vector < / td > < / tr >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > N< / td > < td > number of training samples < / td > < / tr >
< / table >
< / dd >
< / dl >
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< div class = "fragment" > < div class = "line" > < a name = "l00172" > < / a > < span class = "lineno" > 172< / span >   {< / div >
< div class = "line" > < a name = "l00173" > < / a > < span class = "lineno" > 173< / span >   < span class = "keywordtype" > double< / span > avg_pred_error = 1.f;< / div >
< div class = "line" > < a name = "l00174" > < / a > < span class = "lineno" > 174< / span >   < / div >
< div class = "line" > < a name = "l00175" > < / a > < span class = "lineno" > 175< / span >   < span class = "keywordtype" > int< / span > iter;< / div >
< div class = "line" > < a name = "l00176" > < / a > < span class = "lineno" > 176< / span >   < span class = "keywordflow" > for< / span > (iter = 0; (iter < < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#acd517c6f195c75b9dd0f3aad65326f3b" > MAX_ITER< / a > ) & & (avg_pred_error > < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#af270a96662132d0385cb6b4637c5a689" > ACCURACY< / a > ); iter++)< / div >
< div class = "line" > < a name = "l00177" > < / a > < span class = "lineno" > 177< / span >   {< / div >
< div class = "line" > < a name = "l00178" > < / a > < span class = "lineno" > 178< / span >   avg_pred_error = 0.f;< / div >
< div class = "line" > < a name = "l00179" > < / a > < span class = "lineno" > 179< / span >   < / div >
< div class = "line" > < a name = "l00180" > < / a > < span class = "lineno" > 180< / span >   < span class = "comment" > // perform fit for each sample< / span > < / div >
< div class = "line" > < a name = "l00181" > < / a > < span class = "lineno" > 181< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++)< / div >
< div class = "line" > < a name = "l00182" > < / a > < span class = "lineno" > 182< / span >   {< / div >
< div class = "line" > < a name = "l00183" > < / a > < span class = "lineno" > 183< / span >   < span class = "keywordtype" > double< / span > err = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#afa953e811eaae199d121a7ddb619d604" > fit_sample< / a > (ada, X[i], y[i]);< / div >
< div class = "line" > < a name = "l00184" > < / a > < span class = "lineno" > 184< / span >   avg_pred_error += fabs(err);< / div >
< div class = "line" > < a name = "l00185" > < / a > < span class = "lineno" > 185< / span >   }< / div >
< div class = "line" > < a name = "l00186" > < / a > < span class = "lineno" > 186< / span >   avg_pred_error /= < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ;< / div >
< div class = "line" > < a name = "l00187" > < / a > < span class = "lineno" > 187< / span >   < / div >
< div class = "line" > < a name = "l00188" > < / a > < span class = "lineno" > 188< / span >   < span class = "comment" > // Print updates every 200th iteration< / span > < / div >
< div class = "line" > < a name = "l00189" > < / a > < span class = "lineno" > 189< / span >   < span class = "comment" > // if (iter % 100 == 0)< / span > < / div >
< div class = "line" > < a name = "l00190" > < / a > < span class = "lineno" > 190< / span >   printf(< span class = "stringliteral" > " \tIter %3d: Training weights: %s\tAvg error: %.4f\n" < / span > , iter,< / div >
< div class = "line" > < a name = "l00191" > < / a > < span class = "lineno" > 191< / span >   < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (ada), avg_pred_error);< / div >
< div class = "line" > < a name = "l00192" > < / a > < span class = "lineno" > 192< / span >   }< / div >
< div class = "line" > < a name = "l00193" > < / a > < span class = "lineno" > 193< / span >   < / div >
< div class = "line" > < a name = "l00194" > < / a > < span class = "lineno" > 194< / span >   < span class = "keywordflow" > if< / span > (iter < < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#acd517c6f195c75b9dd0f3aad65326f3b" > MAX_ITER< / a > )< / div >
< div class = "line" > < a name = "l00195" > < / a > < span class = "lineno" > 195< / span >   printf(< span class = "stringliteral" > " Converged after %d iterations.\n" < / span > , iter);< / div >
< div class = "line" > < a name = "l00196" > < / a > < span class = "lineno" > 196< / span >   < span class = "keywordflow" > else< / span > < / div >
< div class = "line" > < a name = "l00197" > < / a > < span class = "lineno" > 197< / span >   printf(< span class = "stringliteral" > " Did not converged after %d iterations.\n" < / span > , iter);< / div >
< div class = "line" > < a name = "l00198" > < / a > < span class = "lineno" > 198< / span >   }< / div >
< / div > <!-- fragment --> < div class = "dynheader" >
Here is the call graph for this function:< / div >
< div class = "dyncontent" >
< div class = "center" > < iframe scrolling = "no" frameborder = "0" src = "../../dd/d8c/adaline__learning_8c_a355fda53c238a0c0c07f03dcc021caed_cgraph.svg" width = "448" height = "88" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
< / div >
< / div >
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< / div >
< / div >
< a id = "afa953e811eaae199d121a7ddb619d604" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#afa953e811eaae199d121a7ddb619d604" > ◆ < / a > < / span > fit_sample()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > double fit_sample < / td >
< td > (< / td >
< td class = "paramtype" > struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *  < / td >
< td class = "paramname" > < em > ada< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > const double *  < / td >
< td class = "paramname" > < em > x< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > const int  < / td >
< td class = "paramname" > < em > y< / em >   < / td >
< / tr >
< tr >
< td > < / td >
< td > )< / td >
< td > < / td > < td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > Update the weights of the model using supervised learning for one feature vector. < / p >
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< dl class = "params" > < dt > Parameters< / dt > < dd >
< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > ada< / td > < td > adaline model to fit < / td > < / tr >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > x< / td > < td > feature vector < / td > < / tr >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > y< / td > < td > known output value < / td > < / tr >
< / table >
< / dd >
< / dl >
< dl class = "section return" > < dt > Returns< / dt > < dd > correction factor < / dd > < / dl >
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< div class = "fragment" > < div class = "line" > < a name = "l00146" > < / a > < span class = "lineno" > 146< / span >   {< / div >
< div class = "line" > < a name = "l00147" > < / a > < span class = "lineno" > 147< / span >   < span class = "comment" > /* output of the model with current weights */< / span > < / div >
< div class = "line" > < a name = "l00148" > < / a > < span class = "lineno" > 148< / span >   < span class = "keywordtype" > int< / span > p = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > predict< / a > (ada, x, NULL);< / div >
< div class = "line" > < a name = "l00149" > < / a > < span class = "lineno" > 149< / span >   < span class = "keywordtype" > int< / span > prediction_error = y - p; < span class = "comment" > // error in estimation< / span > < / div >
< div class = "line" > < a name = "l00150" > < / a > < span class = "lineno" > 150< / span >   < span class = "keywordtype" > double< / span > correction_factor = ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a85dbd7cce6195d11ebb388220b96bde2" > eta< / a > * prediction_error;< / div >
< div class = "line" > < a name = "l00151" > < / a > < span class = "lineno" > 151< / span >   < / div >
< div class = "line" > < a name = "l00152" > < / a > < span class = "lineno" > 152< / span >   < span class = "comment" > /* update each weight, the last weight is the bias term */< / span > < / div >
< div class = "line" > < a name = "l00153" > < / a > < span class = "lineno" > 153< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > - 1; i++)< / div >
< div class = "line" > < a name = "l00154" > < / a > < span class = "lineno" > 154< / span >   {< / div >
< div class = "line" > < a name = "l00155" > < / a > < span class = "lineno" > 155< / span >   ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > [i] += correction_factor * x[i];< / div >
< div class = "line" > < a name = "l00156" > < / a > < span class = "lineno" > 156< / span >   }< / div >
< div class = "line" > < a name = "l00157" > < / a > < span class = "lineno" > 157< / span >   ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > [ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > - 1] += correction_factor; < span class = "comment" > // update bias< / span > < / div >
< div class = "line" > < a name = "l00158" > < / a > < span class = "lineno" > 158< / span >   < / div >
< div class = "line" > < a name = "l00159" > < / a > < span class = "lineno" > 159< / span >   < span class = "keywordflow" > return< / span > correction_factor;< / div >
< div class = "line" > < a name = "l00160" > < / a > < span class = "lineno" > 160< / span >   }< / div >
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< / div > <!-- fragment --> < div class = "dynheader" >
Here is the call graph for this function:< / div >
< div class = "dyncontent" >
< div class = "center" > < iframe scrolling = "no" frameborder = "0" src = "../../dd/d8c/adaline__learning_8c_afa953e811eaae199d121a7ddb619d604_cgraph.svg" width = "330" height = "38" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
< / div >
< / div >
< / div >
< / div >
< a id = "acd88962c5f6341e43cbc69b4a7d3485b" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#acd88962c5f6341e43cbc69b4a7d3485b" > ◆ < / a > < / span > new_adaline()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > new_adaline < / td >
< td > (< / td >
< td class = "paramtype" > const int  < / td >
< td class = "paramname" > < em > num_features< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > const double  < / td >
< td class = "paramname" > < em > eta< / em >   < / td >
< / tr >
< tr >
< td > < / td >
< td > )< / td >
< td > < / td > < td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > Default constructor. < / p >
< dl class = "params" > < dt > Parameters< / dt > < dd >
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< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > num_features< / td > < td > number of features present < / td > < / tr >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > eta< / td > < td > learning rate (optional, default=0.1) < / td > < / tr >
< / table >
< / dd >
< / dl >
< dl class = "section return" > < dt > Returns< / dt > < dd > new adaline model < / dd > < / dl >
2020-06-29 04:40:24 +08:00
< div class = "fragment" > < div class = "line" > < a name = "l00053" > < / a > < span class = "lineno" > 53< / span >   {< / div >
< div class = "line" > < a name = "l00054" > < / a > < span class = "lineno" > 54< / span >   < span class = "keywordflow" > if< / span > (eta < = 0.f || eta > = 1.f)< / div >
< div class = "line" > < a name = "l00055" > < / a > < span class = "lineno" > 55< / span >   {< / div >
< div class = "line" > < a name = "l00056" > < / a > < span class = "lineno" > 56< / span >   fprintf(stderr, < span class = "stringliteral" > " learning rate should be > 0 and < 1\n" < / span > );< / div >
< div class = "line" > < a name = "l00057" > < / a > < span class = "lineno" > 57< / span >   exit(EXIT_FAILURE);< / div >
< div class = "line" > < a name = "l00058" > < / a > < span class = "lineno" > 58< / span >   }< / div >
< div class = "line" > < a name = "l00059" > < / a > < span class = "lineno" > 59< / span >   < / div >
< div class = "line" > < a name = "l00060" > < / a > < span class = "lineno" > 60< / span >   < span class = "comment" > // additional weight is for the constant bias term< / span > < / div >
< div class = "line" > < a name = "l00061" > < / a > < span class = "lineno" > 61< / span >   < span class = "keywordtype" > int< / span > num_weights = num_features + 1;< / div >
< div class = "line" > < a name = "l00062" > < / a > < span class = "lineno" > 62< / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "../../d2/daa/structadaline.html" > adaline< / a > ada;< / div >
< div class = "line" > < a name = "l00063" > < / a > < span class = "lineno" > 63< / span >   ada.< a class = "code" href = "../../d2/daa/structadaline.html#a85dbd7cce6195d11ebb388220b96bde2" > eta< / a > = < a class = "code" href = "../../d2/daa/structadaline.html#a85dbd7cce6195d11ebb388220b96bde2" > eta< / a > ;< / div >
< div class = "line" > < a name = "l00064" > < / a > < span class = "lineno" > 64< / span >   ada.num_weights = < a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > ;< / div >
< div class = "line" > < a name = "l00065" > < / a > < span class = "lineno" > 65< / span >   ada.weights = (< span class = "keywordtype" > double< / span > *)malloc(< a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > double< / span > ));< / div >
< div class = "line" > < a name = "l00066" > < / a > < span class = "lineno" > 66< / span >   < span class = "keywordflow" > if< / span > (!ada.weights)< / div >
< div class = "line" > < a name = "l00067" > < / a > < span class = "lineno" > 67< / span >   {< / div >
< div class = "line" > < a name = "l00068" > < / a > < span class = "lineno" > 68< / span >   perror(< span class = "stringliteral" > " Unable to allocate error for weights!" < / span > );< / div >
< div class = "line" > < a name = "l00069" > < / a > < span class = "lineno" > 69< / span >   < span class = "keywordflow" > return< / span > ada;< / div >
< div class = "line" > < a name = "l00070" > < / a > < span class = "lineno" > 70< / span >   }< / div >
< div class = "line" > < a name = "l00071" > < / a > < span class = "lineno" > 71< / span >   < / div >
< div class = "line" > < a name = "l00072" > < / a > < span class = "lineno" > 72< / span >   < span class = "comment" > // initialize with random weights in the range [-50, 49]< / span > < / div >
< div class = "line" > < a name = "l00073" > < / a > < span class = "lineno" > 73< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > ; i++) ada.weights[i] = 1.f;< / div >
< div class = "line" > < a name = "l00074" > < / a > < span class = "lineno" > 74< / span >   < span class = "comment" > // ada.weights[i] = (double)(rand() % 100) - 50);< / span > < / div >
< div class = "line" > < a name = "l00075" > < / a > < span class = "lineno" > 75< / span >   < / div >
< div class = "line" > < a name = "l00076" > < / a > < span class = "lineno" > 76< / span >   < span class = "keywordflow" > return< / span > ada;< / div >
< div class = "line" > < a name = "l00077" > < / a > < span class = "lineno" > 77< / span >   }< / div >
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< / div > <!-- fragment -->
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< a id = "a4db1ba00a7f282100ea31a94e32bd7a3" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#a4db1ba00a7f282100ea31a94e32bd7a3" > ◆ < / a > < / span > predict()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > int predict < / td >
< td > (< / td >
< td class = "paramtype" > struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *  < / td >
< td class = "paramname" > < em > ada< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > const double *  < / td >
< td class = "paramname" > < em > x< / em > , < / td >
< / tr >
< tr >
< td class = "paramkey" > < / td >
< td > < / td >
< td class = "paramtype" > double *  < / td >
< td class = "paramname" > < em > out< / em >   < / td >
< / tr >
< tr >
< td > < / td >
< td > )< / td >
< td > < / td > < td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > predict the output of the model for given set of features < / p >
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< dl class = "params" > < dt > Parameters< / dt > < dd >
< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > ada< / td > < td > adaline model to predict < / td > < / tr >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > x< / td > < td > input vector < / td > < / tr >
< tr > < td class = "paramdir" > [out]< / td > < td class = "paramname" > out< / td > < td > optional argument to return neuron output before applying activation function (< code > NULL< / code > to ignore) < / td > < / tr >
< / table >
< / dd >
< / dl >
< dl class = "section return" > < dt > Returns< / dt > < dd > model prediction output < / dd > < / dl >
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< div class = "fragment" > < div class = "line" > < a name = "l00125" > < / a > < span class = "lineno" > 125< / span >   {< / div >
< div class = "line" > < a name = "l00126" > < / a > < span class = "lineno" > 126< / span >   < span class = "keywordtype" > double< / span > y = ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > [ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > - 1]; < span class = "comment" > // assign bias value< / span > < / div >
< div class = "line" > < a name = "l00127" > < / a > < span class = "lineno" > 127< / span >   < / div >
< div class = "line" > < a name = "l00128" > < / a > < span class = "lineno" > 128< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > - 1; i++) y += x[i] * ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > [i];< / div >
< div class = "line" > < a name = "l00129" > < / a > < span class = "lineno" > 129< / span >   < / div >
< div class = "line" > < a name = "l00130" > < / a > < span class = "lineno" > 130< / span >   if (out) < span class = "comment" > // if out variable is not NULL< / span > < / div >
< div class = "line" > < a name = "l00131" > < / a > < span class = "lineno" > 131< / span >   *out = y;< / div >
< div class = "line" > < a name = "l00132" > < / a > < span class = "lineno" > 132< / span >   < / div >
< div class = "line" > < a name = "l00133" > < / a > < span class = "lineno" > 133< / span >   < span class = "keywordflow" > return< / span > < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a7b12b6513314c975303b5a698608322f" > activation< / a > (y); < span class = "comment" > // quantizer: apply ADALINE threshold function< / span > < / div >
< div class = "line" > < a name = "l00134" > < / a > < span class = "lineno" > 134< / span >   }< / div >
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< / div > <!-- fragment --> < div class = "dynheader" >
Here is the call graph for this function:< / div >
< div class = "dyncontent" >
< div class = "center" > < iframe scrolling = "no" frameborder = "0" src = "../../dd/d8c/adaline__learning_8c_a4db1ba00a7f282100ea31a94e32bd7a3_cgraph.svg" width = "199" height = "38" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
< / div >
< / div >
< / div >
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< a id = "ab4ecb3accf5d9e0263087e7265bbe3a9" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#ab4ecb3accf5d9e0263087e7265bbe3a9" > ◆ < / a > < / span > test1()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > void test1 < / td >
< td > (< / td >
< td class = "paramtype" > double  < / td >
< td class = "paramname" > < em > eta< / em > < / td > < td > )< / td >
< td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > test function to predict points in a 2D coordinate system above the line \(x=y\) as +1 and others as -1. < / p >
< p > Note that each point is defined by 2 values or 2 features. < / p > < dl class = "params" > < dt > Parameters< / dt > < dd >
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< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > eta< / td > < td > learning rate (optional, default=0.01) < / td > < / tr >
< / table >
< / dd >
< / dl >
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< div class = "fragment" > < div class = "line" > < a name = "l00207" > < / a > < span class = "lineno" > 207< / span >   {< / div >
< div class = "line" > < a name = "l00208" > < / a > < span class = "lineno" > 208< / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "../../d2/daa/structadaline.html" > adaline< / a > ada = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#acd88962c5f6341e43cbc69b4a7d3485b" > new_adaline< / a > (2, < a class = "code" href = "../../d2/daa/structadaline.html#a85dbd7cce6195d11ebb388220b96bde2" > eta< / a > ); < span class = "comment" > // 2 features< / span > < / div >
< div class = "line" > < a name = "l00209" > < / a > < span class = "lineno" > 209< / span >   < / div >
< div class = "line" > < a name = "l00210" > < / a > < span class = "lineno" > 210< / span >   < span class = "keyword" > const< / span > < span class = "keywordtype" > int< / span > < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > = 10; < span class = "comment" > // number of sample points< / span > < / div >
< div class = "line" > < a name = "l00211" > < / a > < span class = "lineno" > 211< / span >   < span class = "keyword" > const< / span > < span class = "keywordtype" > double< / span > saved_X[10][2] = {{0, 1}, {1, -2}, {2, 3}, {3, -1},< / div >
< div class = "line" > < a name = "l00212" > < / a > < span class = "lineno" > 212< / span >   {4, 1}, {6, -5}, {-7, -3}, {-8, 5},< / div >
< div class = "line" > < a name = "l00213" > < / a > < span class = "lineno" > 213< / span >   {-9, 2}, {-10, -15}};< / div >
< div class = "line" > < a name = "l00214" > < / a > < span class = "lineno" > 214< / span >   < / div >
< div class = "line" > < a name = "l00215" > < / a > < span class = "lineno" > 215< / span >   < span class = "keywordtype" > double< / span > **X = (< span class = "keywordtype" > double< / span > **)malloc(< a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > double< / span > *));< / div >
< div class = "line" > < a name = "l00216" > < / a > < span class = "lineno" > 216< / span >   < span class = "keyword" > const< / span > < span class = "keywordtype" > int< / span > Y[10] = {1, -1, 1, -1, -1,< / div >
< div class = "line" > < a name = "l00217" > < / a > < span class = "lineno" > 217< / span >   -1, 1, 1, 1, -1}; < span class = "comment" > // corresponding y-values< / span > < / div >
< div class = "line" > < a name = "l00218" > < / a > < span class = "lineno" > 218< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++)< / div >
< div class = "line" > < a name = "l00219" > < / a > < span class = "lineno" > 219< / span >   {< / div >
< div class = "line" > < a name = "l00220" > < / a > < span class = "lineno" > 220< / span >   X[i] = (< span class = "keywordtype" > double< / span > *)saved_X[i];< / div >
< div class = "line" > < a name = "l00221" > < / a > < span class = "lineno" > 221< / span >   }< / div >
< div class = "line" > < a name = "l00222" > < / a > < span class = "lineno" > 222< / span >   < / div >
< div class = "line" > < a name = "l00223" > < / a > < span class = "lineno" > 223< / span >   printf(< span class = "stringliteral" > " ------- Test 1 -------\n" < / span > );< / div >
< div class = "line" > < a name = "l00224" > < / a > < span class = "lineno" > 224< / span >   printf(< span class = "stringliteral" > " Model before fit: %s" < / span > , < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (& ada));< / div >
< div class = "line" > < a name = "l00225" > < / a > < span class = "lineno" > 225< / span >   < / div >
< div class = "line" > < a name = "l00226" > < / a > < span class = "lineno" > 226< / span >   < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a355fda53c238a0c0c07f03dcc021caed" > fit< / a > (& ada, X, Y, < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > );< / div >
< div class = "line" > < a name = "l00227" > < / a > < span class = "lineno" > 227< / span >   printf(< span class = "stringliteral" > " Model after fit: %s\n" < / span > , < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (& ada));< / div >
< div class = "line" > < a name = "l00228" > < / a > < span class = "lineno" > 228< / span >   < / div >
< div class = "line" > < a name = "l00229" > < / a > < span class = "lineno" > 229< / span >   < span class = "keywordtype" > double< / span > test_x[] = {5, -3};< / div >
< div class = "line" > < a name = "l00230" > < / a > < span class = "lineno" > 230< / span >   < span class = "keywordtype" > int< / span > pred = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > predict< / a > (& ada, test_x, NULL);< / div >
< div class = "line" > < a name = "l00231" > < / a > < span class = "lineno" > 231< / span >   printf(< span class = "stringliteral" > " Predict for x=(5,-3): % d" < / span > , pred);< / div >
< div class = "line" > < a name = "l00232" > < / a > < span class = "lineno" > 232< / span >   assert(pred == -1);< / div >
< div class = "line" > < a name = "l00233" > < / a > < span class = "lineno" > 233< / span >   printf(< span class = "stringliteral" > " ...passed\n" < / span > );< / div >
< div class = "line" > < a name = "l00234" > < / a > < span class = "lineno" > 234< / span >   < / div >
< div class = "line" > < a name = "l00235" > < / a > < span class = "lineno" > 235< / span >   < span class = "keywordtype" > double< / span > test_x2[] = {5, 8};< / div >
< div class = "line" > < a name = "l00236" > < / a > < span class = "lineno" > 236< / span >   pred = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > predict< / a > (& ada, test_x2, NULL);< / div >
< div class = "line" > < a name = "l00237" > < / a > < span class = "lineno" > 237< / span >   printf(< span class = "stringliteral" > " Predict for x=(5, 8): % d" < / span > , pred);< / div >
< div class = "line" > < a name = "l00238" > < / a > < span class = "lineno" > 238< / span >   assert(pred == 1);< / div >
< div class = "line" > < a name = "l00239" > < / a > < span class = "lineno" > 239< / span >   printf(< span class = "stringliteral" > " ...passed\n" < / span > );< / div >
< div class = "line" > < a name = "l00240" > < / a > < span class = "lineno" > 240< / span >   < / div >
< div class = "line" > < a name = "l00241" > < / a > < span class = "lineno" > 241< / span >   < span class = "comment" > // for (int i = 0; i < N; i++)< / span > < / div >
< div class = "line" > < a name = "l00242" > < / a > < span class = "lineno" > 242< / span >   < span class = "comment" > // free(X[i]);< / span > < / div >
< div class = "line" > < a name = "l00243" > < / a > < span class = "lineno" > 243< / span >   free(X);< / div >
< div class = "line" > < a name = "l00244" > < / a > < span class = "lineno" > 244< / span >   < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a6f35caa3084772cc126ac7b20f67f665" > delete_adaline< / a > (& ada);< / div >
< div class = "line" > < a name = "l00245" > < / a > < span class = "lineno" > 245< / span >   }< / div >
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< / div > <!-- fragment --> < div class = "dynheader" >
Here is the call graph for this function:< / div >
< div class = "dyncontent" >
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< div class = "center" > < iframe scrolling = "no" frameborder = "0" src = "../../dd/d8c/adaline__learning_8c_ab4ecb3accf5d9e0263087e7265bbe3a9_cgraph.svg" width = "614" height = "240" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
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< / div >
< / div >
< / div >
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< a id = "a05cc9a0acb524fde727a4d7b4a747ee6" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#a05cc9a0acb524fde727a4d7b4a747ee6" > ◆ < / a > < / span > test2()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > void test2 < / td >
< td > (< / td >
< td class = "paramtype" > double  < / td >
< td class = "paramname" > < em > eta< / em > < / td > < td > )< / td >
< td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > test function to predict points in a 2D coordinate system above the line \(x+3y=-1\) as +1 and others as -1. < / p >
< p > Note that each point is defined by 2 values or 2 features. The function will create random sample points for training and test purposes. < / p > < dl class = "params" > < dt > Parameters< / dt > < dd >
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< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > eta< / td > < td > learning rate (optional, default=0.01) < / td > < / tr >
< / table >
< / dd >
< / dl >
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< div class = "fragment" > < div class = "line" > < a name = "l00255" > < / a > < span class = "lineno" > 255< / span >   {< / div >
< div class = "line" > < a name = "l00256" > < / a > < span class = "lineno" > 256< / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "../../d2/daa/structadaline.html" > adaline< / a > ada = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#acd88962c5f6341e43cbc69b4a7d3485b" > new_adaline< / a > (2, < a class = "code" href = "../../d2/daa/structadaline.html#a85dbd7cce6195d11ebb388220b96bde2" > eta< / a > ); < span class = "comment" > // 2 features< / span > < / div >
< div class = "line" > < a name = "l00257" > < / a > < span class = "lineno" > 257< / span >   < / div >
< div class = "line" > < a name = "l00258" > < / a > < span class = "lineno" > 258< / span >   < span class = "keyword" > const< / span > < span class = "keywordtype" > int< / span > < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > = 50; < span class = "comment" > // number of sample points< / span > < / div >
< div class = "line" > < a name = "l00259" > < / a > < span class = "lineno" > 259< / span >   < / div >
< div class = "line" > < a name = "l00260" > < / a > < span class = "lineno" > 260< / span >   < span class = "keywordtype" > double< / span > **X = (< span class = "keywordtype" > double< / span > **)malloc(< a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > double< / span > *));< / div >
< div class = "line" > < a name = "l00261" > < / a > < span class = "lineno" > 261< / span >   < span class = "keywordtype" > int< / span > *Y = (< span class = "keywordtype" > int< / span > *)malloc(< a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > int< / span > )); < span class = "comment" > // corresponding y-values< / span > < / div >
< div class = "line" > < a name = "l00262" > < / a > < span class = "lineno" > 262< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++) X[i] = (< span class = "keywordtype" > double< / span > *)malloc(2 * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > double< / span > ));< / div >
< div class = "line" > < a name = "l00263" > < / a > < span class = "lineno" > 263< / span >   < / div >
< div class = "line" > < a name = "l00264" > < / a > < span class = "lineno" > 264< / span >   < span class = "comment" > // generate sample points in the interval< / span > < / div >
< div class = "line" > < a name = "l00265" > < / a > < span class = "lineno" > 265< / span >   < span class = "comment" > // [-range2/100 , (range2-1)/100]< / span > < / div >
< div class = "line" > < a name = "l00266" > < / a > < span class = "lineno" > 266< / span >   < span class = "keywordtype" > int< / span > < a class = "code" href = "../../df/dea/structdata.html" > range< / a > = 500; < span class = "comment" > // sample points full-range< / span > < / div >
< div class = "line" > < a name = "l00267" > < / a > < span class = "lineno" > 267< / span >   < span class = "keywordtype" > int< / span > range2 = < a class = "code" href = "../../df/dea/structdata.html" > range< / a > > > 1; < span class = "comment" > // sample points half-range< / span > < / div >
< div class = "line" > < a name = "l00268" > < / a > < span class = "lineno" > 268< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++)< / div >
< div class = "line" > < a name = "l00269" > < / a > < span class = "lineno" > 269< / span >   {< / div >
< div class = "line" > < a name = "l00270" > < / a > < span class = "lineno" > 270< / span >   < span class = "keywordtype" > double< / span > x0 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00271" > < / a > < span class = "lineno" > 271< / span >   < span class = "keywordtype" > double< / span > x1 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00272" > < / a > < span class = "lineno" > 272< / span >   X[i][0] = x0;< / div >
< div class = "line" > < a name = "l00273" > < / a > < span class = "lineno" > 273< / span >   X[i][1] = x1;< / div >
< div class = "line" > < a name = "l00274" > < / a > < span class = "lineno" > 274< / span >   Y[i] = (x0 + 3. * x1) > -1 ? 1 : -1;< / div >
< div class = "line" > < a name = "l00275" > < / a > < span class = "lineno" > 275< / span >   }< / div >
< div class = "line" > < a name = "l00276" > < / a > < span class = "lineno" > 276< / span >   < / div >
< div class = "line" > < a name = "l00277" > < / a > < span class = "lineno" > 277< / span >   printf(< span class = "stringliteral" > " ------- Test 2 -------\n" < / span > );< / div >
< div class = "line" > < a name = "l00278" > < / a > < span class = "lineno" > 278< / span >   printf(< span class = "stringliteral" > " Model before fit: %s" < / span > , < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (& ada));< / div >
< div class = "line" > < a name = "l00279" > < / a > < span class = "lineno" > 279< / span >   < / div >
< div class = "line" > < a name = "l00280" > < / a > < span class = "lineno" > 280< / span >   < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a355fda53c238a0c0c07f03dcc021caed" > fit< / a > (& ada, X, Y, < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > );< / div >
< div class = "line" > < a name = "l00281" > < / a > < span class = "lineno" > 281< / span >   printf(< span class = "stringliteral" > " Model after fit: %s\n" < / span > , < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (& ada));< / div >
< div class = "line" > < a name = "l00282" > < / a > < span class = "lineno" > 282< / span >   < / div >
< div class = "line" > < a name = "l00283" > < / a > < span class = "lineno" > 283< / span >   < span class = "keywordtype" > int< / span > N_test_cases = 5;< / div >
< div class = "line" > < a name = "l00284" > < / a > < span class = "lineno" > 284< / span >   < span class = "keywordtype" > double< / span > test_x[2];< / div >
< div class = "line" > < a name = "l00285" > < / a > < span class = "lineno" > 285< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < N_test_cases; i++)< / div >
< div class = "line" > < a name = "l00286" > < / a > < span class = "lineno" > 286< / span >   {< / div >
< div class = "line" > < a name = "l00287" > < / a > < span class = "lineno" > 287< / span >   < span class = "keywordtype" > double< / span > x0 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00288" > < / a > < span class = "lineno" > 288< / span >   < span class = "keywordtype" > double< / span > x1 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00289" > < / a > < span class = "lineno" > 289< / span >   < / div >
< div class = "line" > < a name = "l00290" > < / a > < span class = "lineno" > 290< / span >   test_x[0] = x0;< / div >
< div class = "line" > < a name = "l00291" > < / a > < span class = "lineno" > 291< / span >   test_x[1] = x1;< / div >
< div class = "line" > < a name = "l00292" > < / a > < span class = "lineno" > 292< / span >   < span class = "keywordtype" > int< / span > pred = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > predict< / a > (& ada, test_x, NULL);< / div >
< div class = "line" > < a name = "l00293" > < / a > < span class = "lineno" > 293< / span >   printf(< span class = "stringliteral" > " Predict for x=(% 3.2f,% 3.2f): % d" < / span > , x0, x1, pred);< / div >
< div class = "line" > < a name = "l00294" > < / a > < span class = "lineno" > 294< / span >   < / div >
< div class = "line" > < a name = "l00295" > < / a > < span class = "lineno" > 295< / span >   < span class = "keywordtype" > int< / span > expected_val = (x0 + 3. * x1) > -1 ? 1 : -1;< / div >
< div class = "line" > < a name = "l00296" > < / a > < span class = "lineno" > 296< / span >   assert(pred == expected_val);< / div >
< div class = "line" > < a name = "l00297" > < / a > < span class = "lineno" > 297< / span >   printf(< span class = "stringliteral" > " ...passed\n" < / span > );< / div >
< div class = "line" > < a name = "l00298" > < / a > < span class = "lineno" > 298< / span >   }< / div >
< div class = "line" > < a name = "l00299" > < / a > < span class = "lineno" > 299< / span >   < / div >
< div class = "line" > < a name = "l00300" > < / a > < span class = "lineno" > 300< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++) free(X[i]);< / div >
< div class = "line" > < a name = "l00301" > < / a > < span class = "lineno" > 301< / span >   free(X);< / div >
< div class = "line" > < a name = "l00302" > < / a > < span class = "lineno" > 302< / span >   free(Y);< / div >
< div class = "line" > < a name = "l00303" > < / a > < span class = "lineno" > 303< / span >   < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a6f35caa3084772cc126ac7b20f67f665" > delete_adaline< / a > (& ada);< / div >
< div class = "line" > < a name = "l00304" > < / a > < span class = "lineno" > 304< / span >   }< / div >
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< / div > <!-- fragment --> < div class = "dynheader" >
Here is the call graph for this function:< / div >
< div class = "dyncontent" >
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< div class = "center" > < iframe scrolling = "no" frameborder = "0" src = "../../dd/d8c/adaline__learning_8c_a05cc9a0acb524fde727a4d7b4a747ee6_cgraph.svg" width = "614" height = "240" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
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< / div >
< / div >
< / div >
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< a id = "a3f37b9f073f7e57fd0b39d70718af1b1" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#a3f37b9f073f7e57fd0b39d70718af1b1" > ◆ < / a > < / span > test3()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > void test3 < / td >
< td > (< / td >
< td class = "paramtype" > double  < / td >
< td class = "paramname" > < em > eta< / em > < / td > < td > )< / td >
< td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
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< p > test function to predict points in a 3D coordinate system lying within the sphere of radius 1 and centre at origin as +1 and others as -1. < / p >
< p > Note that each point is defined by 3 values but we use 6 features. The function will create random sample points for training and test purposes. The sphere centred at origin and radius 1 is defined as: \(x^2+y^2+z^2=r^2=1\) and if the \(r^2< 1\), point lies within the sphere else, outside.< / p >
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< dl class = "params" > < dt > Parameters< / dt > < dd >
< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > eta< / td > < td > learning rate (optional, default=0.01) < / td > < / tr >
< / table >
< / dd >
< / dl >
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< div class = "fragment" > < div class = "line" > < a name = "l00318" > < / a > < span class = "lineno" > 318< / span >   {< / div >
< div class = "line" > < a name = "l00319" > < / a > < span class = "lineno" > 319< / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "../../d2/daa/structadaline.html" > adaline< / a > ada = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#acd88962c5f6341e43cbc69b4a7d3485b" > new_adaline< / a > (6, < a class = "code" href = "../../d2/daa/structadaline.html#a85dbd7cce6195d11ebb388220b96bde2" > eta< / a > ); < span class = "comment" > // 2 features< / span > < / div >
< div class = "line" > < a name = "l00320" > < / a > < span class = "lineno" > 320< / span >   < / div >
< div class = "line" > < a name = "l00321" > < / a > < span class = "lineno" > 321< / span >   < span class = "keyword" > const< / span > < span class = "keywordtype" > int< / span > < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > = 50; < span class = "comment" > // number of sample points< / span > < / div >
< div class = "line" > < a name = "l00322" > < / a > < span class = "lineno" > 322< / span >   < / div >
< div class = "line" > < a name = "l00323" > < / a > < span class = "lineno" > 323< / span >   < span class = "keywordtype" > double< / span > **X = (< span class = "keywordtype" > double< / span > **)malloc(< a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > double< / span > *));< / div >
< div class = "line" > < a name = "l00324" > < / a > < span class = "lineno" > 324< / span >   < span class = "keywordtype" > int< / span > *Y = (< span class = "keywordtype" > int< / span > *)malloc(< a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > int< / span > )); < span class = "comment" > // corresponding y-values< / span > < / div >
< div class = "line" > < a name = "l00325" > < / a > < span class = "lineno" > 325< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++) X[i] = (< span class = "keywordtype" > double< / span > *)malloc(6 * < span class = "keyword" > sizeof< / span > (< span class = "keywordtype" > double< / span > ));< / div >
< div class = "line" > < a name = "l00326" > < / a > < span class = "lineno" > 326< / span >   < / div >
< div class = "line" > < a name = "l00327" > < / a > < span class = "lineno" > 327< / span >   < span class = "comment" > // generate sample points in the interval< / span > < / div >
< div class = "line" > < a name = "l00328" > < / a > < span class = "lineno" > 328< / span >   < span class = "comment" > // [-range2/100 , (range2-1)/100]< / span > < / div >
< div class = "line" > < a name = "l00329" > < / a > < span class = "lineno" > 329< / span >   < span class = "keywordtype" > int< / span > < a class = "code" href = "../../df/dea/structdata.html" > range< / a > = 200; < span class = "comment" > // sample points full-range< / span > < / div >
< div class = "line" > < a name = "l00330" > < / a > < span class = "lineno" > 330< / span >   < span class = "keywordtype" > int< / span > range2 = < a class = "code" href = "../../df/dea/structdata.html" > range< / a > > > 1; < span class = "comment" > // sample points half-range< / span > < / div >
< div class = "line" > < a name = "l00331" > < / a > < span class = "lineno" > 331< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++)< / div >
< div class = "line" > < a name = "l00332" > < / a > < span class = "lineno" > 332< / span >   {< / div >
< div class = "line" > < a name = "l00333" > < / a > < span class = "lineno" > 333< / span >   < span class = "keywordtype" > double< / span > x0 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00334" > < / a > < span class = "lineno" > 334< / span >   < span class = "keywordtype" > double< / span > x1 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00335" > < / a > < span class = "lineno" > 335< / span >   < span class = "keywordtype" > double< / span > x2 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00336" > < / a > < span class = "lineno" > 336< / span >   X[i][0] = x0;< / div >
< div class = "line" > < a name = "l00337" > < / a > < span class = "lineno" > 337< / span >   X[i][1] = x1;< / div >
< div class = "line" > < a name = "l00338" > < / a > < span class = "lineno" > 338< / span >   X[i][2] = x2;< / div >
< div class = "line" > < a name = "l00339" > < / a > < span class = "lineno" > 339< / span >   X[i][3] = x0 * x0;< / div >
< div class = "line" > < a name = "l00340" > < / a > < span class = "lineno" > 340< / span >   X[i][4] = x1 * x1;< / div >
< div class = "line" > < a name = "l00341" > < / a > < span class = "lineno" > 341< / span >   X[i][5] = x2 * x2;< / div >
< div class = "line" > < a name = "l00342" > < / a > < span class = "lineno" > 342< / span >   Y[i] = (x0 * x0 + x1 * x1 + x2 * x2) < = 1 ? 1 : -1;< / div >
< div class = "line" > < a name = "l00343" > < / a > < span class = "lineno" > 343< / span >   }< / div >
< div class = "line" > < a name = "l00344" > < / a > < span class = "lineno" > 344< / span >   < / div >
< div class = "line" > < a name = "l00345" > < / a > < span class = "lineno" > 345< / span >   printf(< span class = "stringliteral" > " ------- Test 3 -------\n" < / span > );< / div >
< div class = "line" > < a name = "l00346" > < / a > < span class = "lineno" > 346< / span >   printf(< span class = "stringliteral" > " Model before fit: %s" < / span > , < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (& ada));< / div >
< div class = "line" > < a name = "l00347" > < / a > < span class = "lineno" > 347< / span >   < / div >
< div class = "line" > < a name = "l00348" > < / a > < span class = "lineno" > 348< / span >   < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a355fda53c238a0c0c07f03dcc021caed" > fit< / a > (& ada, X, Y, < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > );< / div >
< div class = "line" > < a name = "l00349" > < / a > < span class = "lineno" > 349< / span >   printf(< span class = "stringliteral" > " Model after fit: %s\n" < / span > , < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > (& ada));< / div >
< div class = "line" > < a name = "l00350" > < / a > < span class = "lineno" > 350< / span >   < / div >
< div class = "line" > < a name = "l00351" > < / a > < span class = "lineno" > 351< / span >   < span class = "keywordtype" > int< / span > N_test_cases = 5;< / div >
< div class = "line" > < a name = "l00352" > < / a > < span class = "lineno" > 352< / span >   < span class = "keywordtype" > double< / span > test_x[6];< / div >
< div class = "line" > < a name = "l00353" > < / a > < span class = "lineno" > 353< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < N_test_cases; i++)< / div >
< div class = "line" > < a name = "l00354" > < / a > < span class = "lineno" > 354< / span >   {< / div >
< div class = "line" > < a name = "l00355" > < / a > < span class = "lineno" > 355< / span >   < span class = "keywordtype" > double< / span > x0 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00356" > < / a > < span class = "lineno" > 356< / span >   < span class = "keywordtype" > double< / span > x1 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00357" > < / a > < span class = "lineno" > 357< / span >   < span class = "keywordtype" > double< / span > x2 = ((rand() % < a class = "code" href = "../../df/dea/structdata.html" > range< / a > ) - range2) / 100.f;< / div >
< div class = "line" > < a name = "l00358" > < / a > < span class = "lineno" > 358< / span >   test_x[0] = x0;< / div >
< div class = "line" > < a name = "l00359" > < / a > < span class = "lineno" > 359< / span >   test_x[1] = x1;< / div >
< div class = "line" > < a name = "l00360" > < / a > < span class = "lineno" > 360< / span >   test_x[2] = x2;< / div >
< div class = "line" > < a name = "l00361" > < / a > < span class = "lineno" > 361< / span >   test_x[3] = x0 * x0;< / div >
< div class = "line" > < a name = "l00362" > < / a > < span class = "lineno" > 362< / span >   test_x[4] = x1 * x1;< / div >
< div class = "line" > < a name = "l00363" > < / a > < span class = "lineno" > 363< / span >   test_x[5] = x2 * x2;< / div >
< div class = "line" > < a name = "l00364" > < / a > < span class = "lineno" > 364< / span >   < span class = "keywordtype" > int< / span > pred = < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > predict< / a > (& ada, test_x, NULL);< / div >
< div class = "line" > < a name = "l00365" > < / a > < span class = "lineno" > 365< / span >   printf(< span class = "stringliteral" > " Predict for x=(% 3.2f,% 3.2f): % d" < / span > , x0, x1, pred);< / div >
< div class = "line" > < a name = "l00366" > < / a > < span class = "lineno" > 366< / span >   < / div >
< div class = "line" > < a name = "l00367" > < / a > < span class = "lineno" > 367< / span >   < span class = "keywordtype" > int< / span > expected_val = (x0 * x0 + x1 * x1 + x2 * x2) < = 1 ? 1 : -1;< / div >
< div class = "line" > < a name = "l00368" > < / a > < span class = "lineno" > 368< / span >   assert(pred == expected_val);< / div >
< div class = "line" > < a name = "l00369" > < / a > < span class = "lineno" > 369< / span >   printf(< span class = "stringliteral" > " ...passed\n" < / span > );< / div >
< div class = "line" > < a name = "l00370" > < / a > < span class = "lineno" > 370< / span >   }< / div >
< div class = "line" > < a name = "l00371" > < / a > < span class = "lineno" > 371< / span >   < / div >
< div class = "line" > < a name = "l00372" > < / a > < span class = "lineno" > 372< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < < a class = "code" href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > ; i++) free(X[i]);< / div >
< div class = "line" > < a name = "l00373" > < / a > < span class = "lineno" > 373< / span >   free(X);< / div >
< div class = "line" > < a name = "l00374" > < / a > < span class = "lineno" > 374< / span >   free(Y);< / div >
< div class = "line" > < a name = "l00375" > < / a > < span class = "lineno" > 375< / span >   < a class = "code" href = "../../dd/d8c/adaline__learning_8c.html#a6f35caa3084772cc126ac7b20f67f665" > delete_adaline< / a > (& ada);< / div >
< div class = "line" > < a name = "l00376" > < / a > < span class = "lineno" > 376< / span >   }< / div >
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< / div > <!-- fragment --> < div class = "dynheader" >
Here is the call graph for this function:< / div >
< div class = "dyncontent" >
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< div class = "ttc" id = "aadaline__learning_8c_html_acd517c6f195c75b9dd0f3aad65326f3b" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#acd517c6f195c75b9dd0f3aad65326f3b" > MAX_ITER< / a > < / div > < div class = "ttdeci" > #define MAX_ITER< / div > < div class = "ttdoc" > Maximum number of iterations to learn.< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:34< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_a00dc6134ca22a12e0fd9cac54d601c2d" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#a00dc6134ca22a12e0fd9cac54d601c2d" > get_weights_str< / a > < / div > < div class = "ttdeci" > char * get_weights_str(struct adaline *ada)< / div > < div class = "ttdoc" > Operator to print the weights of the model.< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:100< / div > < / div >
< div class = "ttc" id = "astructadaline_html_a32e58c03fd9258709eae6138ad0ec657" > < div class = "ttname" > < a href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > adaline::weights< / a > < / div > < div class = "ttdeci" > double * weights< / div > < div class = "ttdoc" > weights of the neural network< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:40< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_a6f35caa3084772cc126ac7b20f67f665" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#a6f35caa3084772cc126ac7b20f67f665" > delete_adaline< / a > < / div > < div class = "ttdeci" > void delete_adaline(struct adaline *ada)< / div > < div class = "ttdoc" > delete dynamically allocated memory< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:82< / div > < / div >
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< div class = "ttc" id = "astructdata_html" > < div class = "ttname" > < a href = "../../df/dea/structdata.html" > data< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > prime_factoriziation.c:25< / div > < / div >
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< div class = "ttc" id = "aproblem__13_2sol1_8c_html_a0240ac851181b84ac374872dc5434ee4" > < div class = "ttname" > < a href = "../../db/d01/problem__13_2sol1_8c.html#a0240ac851181b84ac374872dc5434ee4" > N< / a > < / div > < div class = "ttdeci" > #define N< / div > < div class = "ttdoc" > number of digits of the large number< / div > < div class = "ttdef" > < b > Definition:< / b > sol1.c:109< / div > < / div >
< div class = "ttc" id = "astructadaline_html_a85dbd7cce6195d11ebb388220b96bde2" > < div class = "ttname" > < a href = "../../d2/daa/structadaline.html#a85dbd7cce6195d11ebb388220b96bde2" > adaline::eta< / a > < / div > < div class = "ttdeci" > double eta< / div > < div class = "ttdoc" > learning rate of the algorithm< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:39< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_a4db1ba00a7f282100ea31a94e32bd7a3" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#a4db1ba00a7f282100ea31a94e32bd7a3" > predict< / a > < / div > < div class = "ttdeci" > int predict(struct adaline *ada, const double *x, double *out)< / div > < div class = "ttdoc" > predict the output of the model for given set of features< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:124< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_acd88962c5f6341e43cbc69b4a7d3485b" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#acd88962c5f6341e43cbc69b4a7d3485b" > new_adaline< / a > < / div > < div class = "ttdeci" > struct adaline new_adaline(const int num_features, const double eta)< / div > < div class = "ttdoc" > Default constructor.< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:52< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_afa953e811eaae199d121a7ddb619d604" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#afa953e811eaae199d121a7ddb619d604" > fit_sample< / a > < / div > < div class = "ttdeci" > double fit_sample(struct adaline *ada, const double *x, const int y)< / div > < div class = "ttdoc" > Update the weights of the model using supervised learning for one feature vector.< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:145< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_a7b12b6513314c975303b5a698608322f" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#a7b12b6513314c975303b5a698608322f" > activation< / a > < / div > < div class = "ttdeci" > int activation(double x)< / div > < div class = "ttdoc" > Heaviside activation function< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:95< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_a355fda53c238a0c0c07f03dcc021caed" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#a355fda53c238a0c0c07f03dcc021caed" > fit< / a > < / div > < div class = "ttdeci" > void fit(struct adaline *ada, double **X, const int *y, const int N)< / div > < div class = "ttdoc" > Update the weights of the model using supervised learning for an array of vectors.< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:171< / div > < / div >
< div class = "ttc" id = "astructadaline_html" > < div class = "ttname" > < a href = "../../d2/daa/structadaline.html" > adaline< / a > < / div > < div class = "ttdoc" > structure to hold adaline model parameters< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:38< / div > < / div >
< div class = "ttc" id = "aadaline__learning_8c_html_af270a96662132d0385cb6b4637c5a689" > < div class = "ttname" > < a href = "../../dd/d8c/adaline__learning_8c.html#af270a96662132d0385cb6b4637c5a689" > ACCURACY< / a > < / div > < div class = "ttdeci" > #define ACCURACY< / div > < div class = "ttdoc" > convergence accuracy< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:44< / div > < / div >
< div class = "ttc" id = "astructadaline_html_a53314e737a0a5ff4552a03bcc9dafbc1" > < div class = "ttname" > < a href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > adaline::num_weights< / a > < / div > < div class = "ttdeci" > int num_weights< / div > < div class = "ttdoc" > number of weights of the neural network< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:41< / div > < / div >
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