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< a href = "#nested-classes" > Data Structures< / a > |
< a href = "#define-members" > Macros< / a > |
< a href = "#func-members" > Functions< / a > < / div >
< div class = "headertitle" >
< div class = "title" > Adaline learning algorithm< div class = "ingroups" > < a class = "el" href = "../../d9/d66/group__machine__learning.html" > Machine learning algorithms< / a > < / div > < / div > < / div >
< / div > <!-- header -->
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Collaboration diagram for Adaline learning algorithm:< / div >
< div class = "dyncontent" >
< div class = "center" > < iframe scrolling = "no" frameborder = "0" src = "../../da/d2a/group__adaline.svg" width = "414" height = "36" > < 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 >
< 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 >
< tr class = "separator:" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< / table > < table class = "memberdecls" >
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Macros< / h2 > < / td > < / tr >
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#define  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../da/d2a/group__adaline.html#ga555ba960994e9bccb2029764588f694f" > MAX_ADALINE_ITER< / a >       500< / td > < / tr >
< tr class = "memdesc:ga555ba960994e9bccb2029764588f694f" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Maximum number of iterations to learn. < br / > < / td > < / tr >
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< tr class = "memdesc:gab4d49d73dec94c092b7ffadba55fb020" > < 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:gacd88962c5f6341e43cbc69b4a7d3485b" > < 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 = "../../da/d2a/group__adaline.html#gacd88962c5f6341e43cbc69b4a7d3485b" > new_adaline< / a > (const int num_features, const double eta)< / td > < / tr >
< tr class = "memdesc:gacd88962c5f6341e43cbc69b4a7d3485b" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Default constructor. < a href = "../../da/d2a/group__adaline.html#gacd88962c5f6341e43cbc69b4a7d3485b" > More...< / a > < br / > < / td > < / tr >
< tr class = "separator:gacd88962c5f6341e43cbc69b4a7d3485b" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:ga6f35caa3084772cc126ac7b20f67f665" > < td class = "memItemLeft" align = "right" valign = "top" > void  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../da/d2a/group__adaline.html#ga6f35caa3084772cc126ac7b20f67f665" > delete_adaline< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada)< / td > < / tr >
< tr class = "memdesc:ga6f35caa3084772cc126ac7b20f67f665" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > delete dynamically allocated memory < a href = "../../da/d2a/group__adaline.html#ga6f35caa3084772cc126ac7b20f67f665" > More...< / a > < br / > < / td > < / tr >
< tr class = "separator:ga6f35caa3084772cc126ac7b20f67f665" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:ga43576566b020c4157d4fb28f0dd45cfa" > < td class = "memItemLeft" align = "right" valign = "top" > int  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../da/d2a/group__adaline.html#ga43576566b020c4157d4fb28f0dd45cfa" > adaline_activation< / a > (double x)< / td > < / tr >
< tr class = "memdesc:ga43576566b020c4157d4fb28f0dd45cfa" > < 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" / > < a href = "../../da/d2a/group__adaline.html#ga43576566b020c4157d4fb28f0dd45cfa" > More...< / a > < br / > < / td > < / tr >
< tr class = "separator:ga43576566b020c4157d4fb28f0dd45cfa" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:ga251695a79baa885cafdcf6d8ed4ac120" > < td class = "memItemLeft" align = "right" valign = "top" > char *  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../da/d2a/group__adaline.html#ga251695a79baa885cafdcf6d8ed4ac120" > adaline_get_weights_str< / a > (const struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada)< / td > < / tr >
< tr class = "memdesc:ga251695a79baa885cafdcf6d8ed4ac120" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Operator to print the weights of the model. < a href = "../../da/d2a/group__adaline.html#ga251695a79baa885cafdcf6d8ed4ac120" > More...< / a > < br / > < / td > < / tr >
< tr class = "separator:ga251695a79baa885cafdcf6d8ed4ac120" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:gac70b578aee679005fd336073969c3d94" > < td class = "memItemLeft" align = "right" valign = "top" > int  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../da/d2a/group__adaline.html#gac70b578aee679005fd336073969c3d94" > adaline_predict< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada, const double *x, double *out)< / td > < / tr >
< tr class = "memdesc:gac70b578aee679005fd336073969c3d94" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > predict the output of the model for given set of features < a href = "../../da/d2a/group__adaline.html#gac70b578aee679005fd336073969c3d94" > More...< / a > < br / > < / td > < / tr >
< tr class = "separator:gac70b578aee679005fd336073969c3d94" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< tr class = "memitem:ga20d3642e0a87f36fdb7bf91b023cd166" > < td class = "memItemLeft" align = "right" valign = "top" > double  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../da/d2a/group__adaline.html#ga20d3642e0a87f36fdb7bf91b023cd166" > adaline_fit_sample< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada, const double *x, const int y)< / td > < / tr >
< tr class = "memdesc:ga20d3642e0a87f36fdb7bf91b023cd166" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Update the weights of the model using supervised learning for one feature vector. < a href = "../../da/d2a/group__adaline.html#ga20d3642e0a87f36fdb7bf91b023cd166" > More...< / a > < br / > < / td > < / tr >
< tr class = "separator:ga20d3642e0a87f36fdb7bf91b023cd166" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
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< tr class = "memitem:gaa52120912e32d2893fe1c6d78da5befd" > < td class = "memItemLeft" align = "right" valign = "top" > void  < / td > < td class = "memItemRight" valign = "bottom" > < a class = "el" href = "../../da/d2a/group__adaline.html#gaa52120912e32d2893fe1c6d78da5befd" > adaline_fit< / a > (struct < a class = "el" href = "../../d2/daa/structadaline.html" > adaline< / a > *ada, double **X, const int *y, const int N)< / td > < / tr >
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< tr class = "memdesc:gaa52120912e32d2893fe1c6d78da5befd" > < td class = "mdescLeft" >   < / td > < td class = "mdescRight" > Update the weights of the model using supervised learning for an array of vectors. < a href = "../../da/d2a/group__adaline.html#gaa52120912e32d2893fe1c6d78da5befd" > More...< / a > < br / > < / td > < / tr >
< tr class = "separator:gaa52120912e32d2893fe1c6d78da5befd" > < td class = "memSeparator" colspan = "2" >   < / td > < / tr >
< / table >
< a name = "details" id = "details" > < / a > < h2 class = "groupheader" > Detailed Description< / h2 >
< h2 class = "groupheader" > Function Documentation< / h2 >
< a id = "ga43576566b020c4157d4fb28f0dd45cfa" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#ga43576566b020c4157d4fb28f0dd45cfa" > ◆ < / a > < / span > adaline_activation()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > int adaline_activation < / td >
< td > (< / td >
< td class = "paramtype" > double  < / td >
< td class = "paramname" > < em > x< / em > < / td > < td > )< / td >
< td > < / td >
< / tr >
< / table >
< / div > < div class = "memdoc" >
< p > < 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" / > < / p >
< dl class = "params" > < dt > Parameters< / dt > < dd >
< table class = "params" >
< tr > < td class = "paramname" > x< / td > < td > activation function input < / td > < / tr >
< / table >
< / dd >
< / dl >
< dl class = "section return" > < dt > Returns< / dt > < dd > \(f(x)= \begin{cases}1 & \forall\; x > 0\\ -1 & \forall\; x \le0 \end{cases}\) < / dd > < / dl >
< div class = "fragment" > < div class = "line" > < a name = "l00105" > < / a > < span class = "lineno" > 105< / span >   { < span class = "keywordflow" > return< / span > x > 0 ? 1 : -1; }< / div >
< / div > <!-- fragment -->
< / div >
< / div >
< a id = "gaa52120912e32d2893fe1c6d78da5befd" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#gaa52120912e32d2893fe1c6d78da5befd" > ◆ < / a > < / span > adaline_fit()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > void adaline_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" >
< p > Update the weights of the model using supervised learning for an array of vectors. < / p >
< 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 >
< div class = "fragment" > < 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 >   < span class = "keywordtype" > double< / span > avg_pred_error = 1.f;< / 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 = "keywordtype" > int< / span > iter;< / div >
< div class = "line" > < a name = "l00189" > < / a > < span class = "lineno" > 189< / span >   < span class = "keywordflow" > for< / span > (iter = 0;< / div >
< div class = "line" > < a name = "l00190" > < / a > < span class = "lineno" > 190< / span >   (iter < < a class = "code" href = "../../da/d2a/group__adaline.html#ga555ba960994e9bccb2029764588f694f" > MAX_ADALINE_ITER< / a > ) & & (avg_pred_error > < a class = "code" href = "../../da/d2a/group__adaline.html#gab4d49d73dec94c092b7ffadba55fb020" > ADALINE_ACCURACY< / a > );< / div >
< div class = "line" > < a name = "l00191" > < / a > < span class = "lineno" > 191< / span >   iter++)< / 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 >   avg_pred_error = 0.f;< / div >
< div class = "line" > < a name = "l00194" > < / a > < span class = "lineno" > 194< / span >   < / div >
< div class = "line" > < a name = "l00195" > < / a > < span class = "lineno" > 195< / span >   < span class = "comment" > // perform fit for each sample< / span > < / div >
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< div class = "line" > < a name = "l00196" > < / a > < span class = "lineno" > 196< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < N; i++)< / div >
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< div class = "line" > < a name = "l00197" > < / a > < span class = "lineno" > 197< / span >   {< / div >
< div class = "line" > < a name = "l00198" > < / a > < span class = "lineno" > 198< / span >   < span class = "keywordtype" > double< / span > err = < a class = "code" href = "../../da/d2a/group__adaline.html#ga20d3642e0a87f36fdb7bf91b023cd166" > adaline_fit_sample< / a > (ada, X[i], y[i]);< / div >
< div class = "line" > < a name = "l00199" > < / a > < span class = "lineno" > 199< / span >   avg_pred_error += fabs(err);< / div >
< div class = "line" > < a name = "l00200" > < / a > < span class = "lineno" > 200< / span >   }< / div >
2020-07-17 04:36:47 +08:00
< div class = "line" > < a name = "l00201" > < / a > < span class = "lineno" > 201< / span >   avg_pred_error /= N;< / div >
2020-07-04 02:15:35 +08:00
< div class = "line" > < a name = "l00202" > < / a > < span class = "lineno" > 202< / span >   < / div >
< div class = "line" > < a name = "l00203" > < / a > < span class = "lineno" > 203< / span >   < span class = "comment" > // Print updates every 200th iteration< / span > < / div >
< div class = "line" > < a name = "l00204" > < / a > < span class = "lineno" > 204< / span >   < span class = "comment" > // if (iter % 100 == 0)< / span > < / div >
< div class = "line" > < a name = "l00205" > < / a > < span class = "lineno" > 205< / span >   printf(< span class = "stringliteral" > " \tIter %3d: Training weights: %s\tAvg error: %.4f\n" < / span > , iter,< / div >
< div class = "line" > < a name = "l00206" > < / a > < span class = "lineno" > 206< / span >   < a class = "code" href = "../../da/d2a/group__adaline.html#ga251695a79baa885cafdcf6d8ed4ac120" > adaline_get_weights_str< / a > (ada), avg_pred_error);< / div >
< 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 >   < / div >
< div class = "line" > < a name = "l00209" > < / a > < span class = "lineno" > 209< / span >   < span class = "keywordflow" > if< / span > (iter < < a class = "code" href = "../../da/d2a/group__adaline.html#ga555ba960994e9bccb2029764588f694f" > MAX_ADALINE_ITER< / a > )< / div >
< div class = "line" > < a name = "l00210" > < / a > < span class = "lineno" > 210< / span >   printf(< span class = "stringliteral" > " Converged after %d iterations.\n" < / span > , iter);< / div >
< div class = "line" > < a name = "l00211" > < / a > < span class = "lineno" > 211< / span >   < span class = "keywordflow" > else< / span > < / div >
< div class = "line" > < a name = "l00212" > < / a > < span class = "lineno" > 212< / span >   printf(< span class = "stringliteral" > " Did not converged after %d iterations.\n" < / span > , iter);< / div >
< div class = "line" > < a name = "l00213" > < / a > < span class = "lineno" > 213< / 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 = "../../da/d2a/group__adaline_gaa52120912e32d2893fe1c6d78da5befd_cgraph.svg" width = "635" height = "88" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
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< a id = "ga20d3642e0a87f36fdb7bf91b023cd166" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#ga20d3642e0a87f36fdb7bf91b023cd166" > ◆ < / a > < / span > adaline_fit_sample()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > double adaline_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" >
< p > Update the weights of the model using supervised learning for one feature vector. < / p >
< 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 >
< div class = "fragment" > < div class = "line" > < a name = "l00159" > < / a > < span class = "lineno" > 159< / span >   {< / div >
< div class = "line" > < a name = "l00160" > < / a > < span class = "lineno" > 160< / span >   < span class = "comment" > /* output of the model with current weights */< / span > < / div >
< div class = "line" > < a name = "l00161" > < / a > < span class = "lineno" > 161< / span >   < span class = "keywordtype" > int< / span > p = < a class = "code" href = "../../da/d2a/group__adaline.html#gac70b578aee679005fd336073969c3d94" > adaline_predict< / a > (ada, x, NULL);< / div >
< div class = "line" > < a name = "l00162" > < / a > < span class = "lineno" > 162< / span >   < span class = "keywordtype" > int< / span > prediction_error = y - p; < span class = "comment" > // error in estimation< / span > < / div >
< div class = "line" > < a name = "l00163" > < / a > < span class = "lineno" > 163< / 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 = "l00164" > < / a > < span class = "lineno" > 164< / span >   < / div >
< div class = "line" > < a name = "l00165" > < / a > < span class = "lineno" > 165< / span >   < span class = "comment" > /* update each weight, the last weight is the bias term */< / span > < / div >
< div class = "line" > < a name = "l00166" > < / a > < span class = "lineno" > 166< / 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 = "l00167" > < / a > < span class = "lineno" > 167< / span >   {< / div >
< div class = "line" > < a name = "l00168" > < / a > < span class = "lineno" > 168< / span >   ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > [i] += correction_factor * x[i];< / div >
< div class = "line" > < a name = "l00169" > < / a > < span class = "lineno" > 169< / span >   }< / div >
< div class = "line" > < a name = "l00170" > < / a > < span class = "lineno" > 170< / 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 = "l00171" > < / a > < span class = "lineno" > 171< / span >   < / div >
< div class = "line" > < a name = "l00172" > < / a > < span class = "lineno" > 172< / span >   < span class = "keywordflow" > return< / span > correction_factor;< / div >
< div class = "line" > < a name = "l00173" > < / a > < span class = "lineno" > 173< / 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 = "../../da/d2a/group__adaline_ga20d3642e0a87f36fdb7bf91b023cd166_cgraph.svg" width = "474" height = "38" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
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< a id = "ga251695a79baa885cafdcf6d8ed4ac120" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#ga251695a79baa885cafdcf6d8ed4ac120" > ◆ < / a > < / span > adaline_get_weights_str()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > char* adaline_get_weights_str < / td >
< td > (< / td >
< td class = "paramtype" > const 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" >
< p > Operator to print the weights of the model. < / p >
< dl class = "params" > < dt > Parameters< / dt > < dd >
< table class = "params" >
< tr > < td class = "paramname" > ada< / td > < td > model for which the values to print < / td > < / tr >
< / table >
< / dd >
< / dl >
< dl class = "section return" > < dt > Returns< / dt > < dd > pointer to a NULL terminated string of formatted weights < / dd > < / dl >
< div class = "fragment" > < div class = "line" > < a name = "l00113" > < / a > < span class = "lineno" > 113< / span >   {< / div >
< div class = "line" > < a name = "l00114" > < / a > < span class = "lineno" > 114< / span >   < span class = "keyword" > static< / span > < span class = "keywordtype" > char< / span > out[100]; < span class = "comment" > // static so the value is persistent< / span > < / div >
< div class = "line" > < a name = "l00115" > < / a > < span class = "lineno" > 115< / span >   < / div >
< div class = "line" > < a name = "l00116" > < / a > < span class = "lineno" > 116< / span >   sprintf(out, < span class = "stringliteral" > " < " < / span > );< / div >
< div class = "line" > < a name = "l00117" > < / a > < span class = "lineno" > 117< / 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 > ; i++)< / div >
< div class = "line" > < a name = "l00118" > < / a > < span class = "lineno" > 118< / span >   {< / div >
< div class = "line" > < a name = "l00119" > < / a > < span class = "lineno" > 119< / span >   sprintf(out, < span class = "stringliteral" > " %s%.4g" < / span > , out, ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > [i]);< / div >
< div class = "line" > < a name = "l00120" > < / a > < span class = "lineno" > 120< / span >   < span class = "keywordflow" > if< / span > (i < ada-> num_weights - 1)< / div >
< div class = "line" > < a name = "l00121" > < / a > < span class = "lineno" > 121< / span >   sprintf(out, < span class = "stringliteral" > " %s, " < / span > , out);< / div >
< div class = "line" > < a name = "l00122" > < / a > < span class = "lineno" > 122< / span >   }< / div >
< div class = "line" > < a name = "l00123" > < / a > < span class = "lineno" > 123< / span >   sprintf(out, < span class = "stringliteral" > " %s> " < / span > , out);< / div >
< div class = "line" > < a name = "l00124" > < / a > < span class = "lineno" > 124< / span >   < span class = "keywordflow" > return< / span > out;< / div >
< div class = "line" > < a name = "l00125" > < / a > < span class = "lineno" > 125< / span >   }< / div >
< / div > <!-- fragment -->
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< / div >
< a id = "gac70b578aee679005fd336073969c3d94" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#gac70b578aee679005fd336073969c3d94" > ◆ < / a > < / span > adaline_predict()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< td class = "memname" > int adaline_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" >
< p > predict the output of the model for given set of features < / p >
< 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 >
< div class = "fragment" > < div class = "line" > < a name = "l00137" > < / a > < span class = "lineno" > 137< / span >   {< / div >
< div class = "line" > < a name = "l00138" > < / a > < span class = "lineno" > 138< / 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 = "l00139" > < / a > < span class = "lineno" > 139< / span >   < / div >
< div class = "line" > < a name = "l00140" > < / a > < span class = "lineno" > 140< / 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 = "l00141" > < / a > < span class = "lineno" > 141< / span >   < / div >
< div class = "line" > < a name = "l00142" > < / a > < span class = "lineno" > 142< / span >   if (out) < span class = "comment" > // if out variable is not NULL< / span > < / div >
< div class = "line" > < a name = "l00143" > < / a > < span class = "lineno" > 143< / span >   *out = y;< / div >
< div class = "line" > < a name = "l00144" > < / a > < span class = "lineno" > 144< / span >   < / div >
< div class = "line" > < a name = "l00145" > < / a > < span class = "lineno" > 145< / span >   < span class = "comment" > // quantizer: apply ADALINE threshold function< / span > < / div >
< div class = "line" > < a name = "l00146" > < / a > < span class = "lineno" > 146< / span >   < span class = "keywordflow" > return< / span > < a class = "code" href = "../../da/d2a/group__adaline.html#ga43576566b020c4157d4fb28f0dd45cfa" > adaline_activation< / a > (y);< / div >
< div class = "line" > < a name = "l00147" > < / a > < span class = "lineno" > 147< / 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 = "../../da/d2a/group__adaline_gac70b578aee679005fd336073969c3d94_cgraph.svg" width = "295" height = "38" > < p > < b > This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.< / b > < / p > < / iframe >
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< a id = "ga6f35caa3084772cc126ac7b20f67f665" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#ga6f35caa3084772cc126ac7b20f67f665" > ◆ < / a > < / span > delete_adaline()< / h2 >
< div class = "memitem" >
< div class = "memproto" >
< table class = "memname" >
< tr >
< 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" >
< p > delete dynamically allocated memory < / p >
< dl class = "params" > < dt > Parameters< / dt > < dd >
< table class = "params" >
< tr > < td class = "paramdir" > [in]< / td > < td class = "paramname" > ada< / td > < td > model from which the memory is to be freed. < / td > < / tr >
< / table >
< / dd >
< / dl >
< div class = "fragment" > < div class = "line" > < a name = "l00090" > < / a > < span class = "lineno" > 90< / span >   {< / div >
< div class = "line" > < a name = "l00091" > < / a > < span class = "lineno" > 91< / span >   < span class = "keywordflow" > if< / span > (ada == NULL)< / div >
< div class = "line" > < a name = "l00092" > < / a > < span class = "lineno" > 92< / span >   < span class = "keywordflow" > return< / span > ;< / div >
< div class = "line" > < a name = "l00093" > < / a > < span class = "lineno" > 93< / span >   < / div >
< div class = "line" > < a name = "l00094" > < / a > < span class = "lineno" > 94< / span >   free(ada-> < a class = "code" href = "../../d2/daa/structadaline.html#a32e58c03fd9258709eae6138ad0ec657" > weights< / a > );< / div >
< div class = "line" > < a name = "l00095" > < / a > < span class = "lineno" > 95< / span >   };< / div >
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< a id = "gacd88962c5f6341e43cbc69b4a7d3485b" > < / a >
< h2 class = "memtitle" > < span class = "permalink" > < a href = "#gacd88962c5f6341e43cbc69b4a7d3485b" > ◆ < / 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" >
< p > Default constructor. < / p >
< dl class = "params" > < dt > Parameters< / dt > < dd >
< 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 >
< div class = "fragment" > < div class = "line" > < a name = "l00060" > < / a > < span class = "lineno" > 60< / span >   {< / div >
< div class = "line" > < a name = "l00061" > < / a > < span class = "lineno" > 61< / span >   < span class = "keywordflow" > if< / span > (eta < = 0.f || eta > = 1.f)< / div >
< div class = "line" > < a name = "l00062" > < / a > < span class = "lineno" > 62< / span >   {< / div >
< div class = "line" > < a name = "l00063" > < / a > < span class = "lineno" > 63< / span >   fprintf(stderr, < span class = "stringliteral" > " learning rate should be > 0 and < 1\n" < / span > );< / div >
< div class = "line" > < a name = "l00064" > < / a > < span class = "lineno" > 64< / span >   exit(EXIT_FAILURE);< / div >
< div class = "line" > < a name = "l00065" > < / a > < span class = "lineno" > 65< / span >   }< / div >
< div class = "line" > < a name = "l00066" > < / a > < span class = "lineno" > 66< / span >   < / div >
< div class = "line" > < a name = "l00067" > < / a > < span class = "lineno" > 67< / span >   < span class = "comment" > // additional weight is for the constant bias term< / span > < / div >
< div class = "line" > < a name = "l00068" > < / a > < span class = "lineno" > 68< / span >   < span class = "keywordtype" > int< / span > num_weights = num_features + 1;< / div >
< div class = "line" > < a name = "l00069" > < / a > < span class = "lineno" > 69< / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "../../d2/daa/structadaline.html" > adaline< / a > ada;< / div >
< div class = "line" > < a name = "l00070" > < / a > < span class = "lineno" > 70< / 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 = "l00071" > < / a > < span class = "lineno" > 71< / span >   ada.num_weights = < a class = "code" href = "../../d2/daa/structadaline.html#a53314e737a0a5ff4552a03bcc9dafbc1" > num_weights< / a > ;< / div >
< div class = "line" > < a name = "l00072" > < / a > < span class = "lineno" > 72< / 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 = "l00073" > < / a > < span class = "lineno" > 73< / span >   < span class = "keywordflow" > if< / span > (!ada.weights)< / div >
< div class = "line" > < a name = "l00074" > < / a > < span class = "lineno" > 74< / span >   {< / div >
< div class = "line" > < a name = "l00075" > < / a > < span class = "lineno" > 75< / span >   perror(< span class = "stringliteral" > " Unable to allocate error for weights!" < / 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 >
< div class = "line" > < a name = "l00078" > < / a > < span class = "lineno" > 78< / span >   < / div >
< div class = "line" > < a name = "l00079" > < / a > < span class = "lineno" > 79< / span >   < span class = "comment" > // initialize with random weights in the range [-50, 49]< / span > < / div >
< div class = "line" > < a name = "l00080" > < / a > < span class = "lineno" > 80< / 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 = "l00081" > < / a > < span class = "lineno" > 81< / span >   < span class = "comment" > // ada.weights[i] = (double)(rand() % 100) - 50);< / span > < / div >
< div class = "line" > < a name = "l00082" > < / a > < span class = "lineno" > 82< / span >   < / div >
< div class = "line" > < a name = "l00083" > < / a > < span class = "lineno" > 83< / span >   < span class = "keywordflow" > return< / span > ada;< / div >
< div class = "line" > < a name = "l00084" > < / a > < span class = "lineno" > 84< / span >   }< / div >
< / div > <!-- fragment -->
< / div >
< / div >
< / div > <!-- contents -->
< / div > <!-- doc - content -->
< 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:46< / div > < / div >
< div class = "ttc" id = "agroup__adaline_html_ga43576566b020c4157d4fb28f0dd45cfa" > < div class = "ttname" > < a href = "../../da/d2a/group__adaline.html#ga43576566b020c4157d4fb28f0dd45cfa" > adaline_activation< / a > < / div > < div class = "ttdeci" > int adaline_activation(double x)< / div > < div class = "ttdoc" > Heaviside activation function< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:105< / div > < / div >
< div class = "ttc" id = "agroup__adaline_html_gab4d49d73dec94c092b7ffadba55fb020" > < div class = "ttname" > < a href = "../../da/d2a/group__adaline.html#gab4d49d73dec94c092b7ffadba55fb020" > ADALINE_ACCURACY< / a > < / div > < div class = "ttdeci" > #define ADALINE_ACCURACY< / div > < div class = "ttdoc" > convergence accuracy< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:51< / div > < / div >
< div class = "ttc" id = "agroup__adaline_html_ga20d3642e0a87f36fdb7bf91b023cd166" > < div class = "ttname" > < a href = "../../da/d2a/group__adaline.html#ga20d3642e0a87f36fdb7bf91b023cd166" > adaline_fit_sample< / a > < / div > < div class = "ttdeci" > double adaline_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:158< / 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:45< / div > < / div >
< div class = "ttc" id = "agroup__adaline_html_ga555ba960994e9bccb2029764588f694f" > < div class = "ttname" > < a href = "../../da/d2a/group__adaline.html#ga555ba960994e9bccb2029764588f694f" > MAX_ADALINE_ITER< / a > < / div > < div class = "ttdeci" > #define MAX_ADALINE_ITER< / div > < div class = "ttdoc" > Maximum number of iterations to learn.< / div > < div class = "ttdef" > < b > Definition:< / b > adaline_learning.c:40< / div > < / div >
< div class = "ttc" id = "agroup__adaline_html_gac70b578aee679005fd336073969c3d94" > < div class = "ttname" > < a href = "../../da/d2a/group__adaline.html#gac70b578aee679005fd336073969c3d94" > adaline_predict< / a > < / div > < div class = "ttdeci" > int adaline_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:136< / 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:44< / div > < / div >
< div class = "ttc" id = "agroup__adaline_html_ga251695a79baa885cafdcf6d8ed4ac120" > < div class = "ttname" > < a href = "../../da/d2a/group__adaline.html#ga251695a79baa885cafdcf6d8ed4ac120" > adaline_get_weights_str< / a > < / div > < div class = "ttdeci" > char * adaline_get_weights_str(const 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:112< / 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:47< / div > < / div >
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