usage.h

基于稀疏网络的精选机器学习模型

"\n",
"     -E {string}\n",
"        Specifies the name of a file in which to write information about\n",
"        mistakes during testing.  No default.\n",
"\n",
"     -f <+ | ->\n",
"        '+' enables automatic insertion of the \"fixed\" feature into every\n",
"        example.  This feature's weight then acts as a dynamic threshold.\n",
"        Default '+'.\n",
"\n",
"     -L {long}\n",
"        Specifies the limit to the number of targets printed with the -o\n",
"        parameter.  This option makes sense only when used in conjunction with\n",
"        the -o parameter.  Default ULONG_MAX.\n",
"\n",
"     -o < accuracy | winners | softmax | allpredictions | allactivations |\n",
"          allboth >\n",
"        See its complete description in the -test mode options section.\n",
"        Default 'accuracy'.\n",
"\n",
"     -p {double}\n",
"        Specifies the prediction threshold.  SNoW will not make a prediction\n",
"        if the activation of the most activated target minus the activation of\n",
"        the second most activated target is less than or equal to the\n",
"        prediction threshold.  Default -1.\n",
"\n",
"     -R {string}\n",
"        Specifies the name of a file in which to write the testing results.\n",
"        Default STDOUT.\n",
"\n",
"     -v <off | min | med | max>\n",
"        Specifies verbosity level.  Default 'min'.\n",
"\n",
"     -w {double}\n",
"        Specifies a smoothing value for Winnow and Perceptron learners.\n",
"        Default 0.\n",
"\n\n",
"     Required options for mode -evaluate:\n",
"\n",
"     -F {string}\n",
"        Specifies the name of a file from which the network is read.\n",
"\n",
"     -x {string}\n",
"        Specifies the example which is to be evaluated.  If the example has\n",
"        any white space or parentheses for specifying feature strengths, it\n",
"        must be surrounded by quotes.\n",
"\n",
"     Other options for modes -evaluate:\n",
"\n",
"     -b {double}\n",
"        Specifies the smoothing parameter to be used in Naive Bayes.  Default\n",
"        15.0.\n",
"\n",
"     -e <count:{integer} | percent:{double}>\n",
"        Sets the feature eligibility method.  In testing mode,\n",
"        -e count:{integer} is useful when reading in and initializing the\n",
"        network.  Any feature whose active count (as recorded in the network)\n",
"        is lower than the eligibility threshold specified will be initialized\n",
"        as 'pending' (waiting to be linked).  Default is 'count:2'.\n",
"\n",
"     -f <+ | ->\n",
"        '+' enables automatic insertion of the \"fixed\" feature into every\n",
"        example.  This feature's weight then acts as a dynamic threshold.\n",
"        Default '+'.\n",
"\n",
"     -m <+ | ->\n",
"        This option specifies whether to train with multiple labels.  '+'\n",
"        means that a given target will not treat other targets' IDs as\n",
"        features when they are encountered in an example.  '-' means that a\n",
"        given target treats all IDs as features except for its own ID.  When\n",
"        making predictions, having multiple targets set to '+' will cause all\n",
"        examples that contain the predicted target anywhere in the example to\n",
"        be counted correct.  If set to '-', the predicted target must be the\n",
"        first target to appear in the example for the example to be counted\n",
"        correct.  This option will have an affect on the smoothing of\n",
"        previously unseen features (see -b and -w).  If set to '-', a given\n",
"        target will treat other target IDs as features which can potentially\n",
"        be smoothed.  Default '+'.\n",
"\n",
"     -p {double}\n",
"        Specifies the prediction threshold.  SNoW will not make a prediction\n",
"        if the activation of the most activated target minus the activation of\n",
"        the second most activated target is less than or equal to the\n",
"        prediction threshold.  Default -1.\n",
"\n",
"     -R {string}\n",
"        Specifies the name of a file in which to write the testing results.\n",
"        Default STDOUT.\n",
"\n",
"     -V <+ | ->\n",
"        '+' enables averaged voted Perceptron testing in conjunction with\n",
"        either Perceptron or Winnow.  Two weight vectors are stored in the\n",
"        network file for each target node when the network is trained with\n",
"        '-V +'; one is the usual weight vector and the other is the average\n",
"        of all incarnations of the weight vector during training, weighted by\n",
"        the number of examples they were applied to.  In testing, this flag\n",
"        directs SNoW to use the averaged weight vector when classifying\n",
"        testing examples.  Default '-'.\n",
"\n",
"     -v <off | min | med | max>\n",
"        Specifies verbosity level.  Default 'min'.\n",
"\n",
"     -w {double}\n",
"        Specifies a smoothing value for Winnow and Perceptron learners.\n",
"        Default 0.\n",
"\n",
"     -z <+ | ->\n",
"        Setting this option to '+' enables \"conventional\" or \"raw\" mode, in\n",
"        which SNoW adheres to the most traditional definitions of its\n",
"        algorithms.  The network is full instead of sparse, features are\n",
"        immediately linked to all targets, and activations are not normalized.\n",
"        Default '-'.\n",
"\n\n",
#ifdef SERVER_MODE_
"     Required options for mode -server:\n",
"\n",
"     -server {integer}\n",
"        Immediately after the -server argument, the port on which the server\n",
"        will listen for clients must be specified.\n",
"\n",
"     -F {string}\n",
"        Specifies the name of a file from which the network is read.\n",
"\n",
"     Other options for modes -server:\n",
"\n",
"     -b {double}\n",
"        Specifies the smoothing parameter to be used in Naive Bayes.  Default\n",
"        15.0.\n",
"\n",
"     -e <count:{integer} | percent:{double}>\n",
"        Sets the feature eligibility method.  In testing mode,\n",
"        -e count:{integer} is useful when reading in and initializing the\n",
"        network.  Any feature whose active count (as recorded in the network)\n",
"        is lower than the eligibility threshold specified will be initialized\n",
"        as 'pending' (waiting to be linked).  Default is 'count:2'.\n",
"\n",
"     -f <+ | ->\n",
"        '+' enables automatic insertion of the \"fixed\" feature into every\n",
"        example.  This feature's weight then acts as a dynamic threshold.\n",
"        Default '+'.\n",
"\n",
"     -L {long}\n",
"        Specifies the limit to the number of targets printed with the -o\n",
"        parameter.  This option makes sense only when used in conjunction with\n",
"        the -o parameter.  Default ULONG_MAX.\n",
"\n",
"     -l <+ | ->\n",
"        This option specifies whether test examples are labeled or not.\n",
"        Default '+'.\n",
"\n",
"     -m <+ | ->\n",
"        This option specifies whether to train with multiple labels.  '+'\n",
"        means that a given target will not treat other targets' IDs as\n",
"        features when they are encountered in an example.  '-' means that a\n",
"        given target treats all IDs as features except for its own ID.  When\n",
"        making predictions, having multiple targets set to '+' will cause all\n",
"        examples that contain the predicted target anywhere in the example to\n",
"        be counted correct.  If set to '-', the predicted target must be the\n",
"        first target to appear in the example for the example to be counted\n",
"        correct.  This option will have an affect on the smoothing of\n",
"        previously unseen features (see -b and -w).  If set to '-', a given\n",
"        target will treat other target IDs as features which can potentially\n",
"        be smoothed.  Default '+'.\n",
"\n",
"     -o < accuracy | winners | softmax | allpredictions | allactivations |\n",
"          allboth >\n",
"        Specifies which output mode to use when reporting test results.\n",
"        Default 'winners'.\n",
"        accuracy:\n",
"          This mode requires labeled examples.  Output is given as a\n",
"          percentage accuracy for all of the test examples.\n",
"        winners:\n",
"          This mode outputs the targetID with the highest activation for each\n",
"          example.\n",
"        softmax:\n",
"          This mode outputs, for each example, a list of all targets and their\n",
"          corresponding softmax normalized activations.\n",
"        allpredictions:\n",
"          This mode outputs, for each example, a list of all targets and their\n",
"          predictions (0 for false, 1 for true).  The target with the highest\n",
"          activation will be predicted as true, all others false.\n",
"        allactivations:\n",
"          This mode outputs, for each example, a list of all targets and their\n",
"          activations.\n",
"        allboth:\n",
"          This mode outputs, for each example, a list of all targets and both\n",
"          their activations and predictions.\n",
"\n",
"     -p {double}\n",
"        Specifies the prediction threshold.  SNoW will not make a prediction\n",
"        if the activation of the most activated target minus the activation of\n",
"        the second most activated target is less than or equal to the\n",
"        prediction threshold.  Default -1.\n",
"\n",
"     -V <+ | ->\n",
"        '+' enables averaged voted Perceptron testing in conjunction with\n",
"        either Perceptron or Winnow.  Two weight vectors are stored in the\n",
"        network file for each target node when the network is trained with\n",
"        '-V +'; one is the usual weight vector and the other is the average\n",
"        of all incarnations of the weight vector during training, weighted by\n",
"        the number of examples they were applied to.  In testing, this flag\n",
"        directs SNoW to use the averaged weight vector when classifying\n",
"        testing examples.  Default '-'.\n",
"\n",
"     -v <off | min | med | max>\n",
"        Specifies verbosity level.  Default 'min'.\n",
"\n",
"     -w {double}\n",
"        Specifies a smoothing value for Winnow and Perceptron learners.\n",
"        Default 0.\n",
"\n",
"     -z <+ | ->\n",
"        Setting this option to '+' enables \"conventional\" or \"raw\" mode, in\n",
"        which SNoW adheres to the most traditional definitions of its\n",
"        algorithms.  The network is full instead of sparse, features are\n",
"        immediately linked to all targets, and activations are not normalized.\n",
"        Default '-'.\n",
"\n\n",
#endif
"EXAMPLES\n",
"\n",
"      snow -train -F mynetwork.net -I train.txt -W 2,0.5:0-3,6\n",
"\n",
"      This invokes SNoW in training mode with network file \"mynetwork.net\",\n",
"      trains from examples in file \"train.txt\", and uses a Winnow algorithm\n",
"      with parameters alpha = 2, beta = 0.5, and targets 0, 1, 2, 3, and 6.\n",
"\n",
"      snow -test -F mynetwork.net -I test.txt -o allactivations -R results.txt\n",
"\n",
"      This invokes SNoW in testing mode, testing the network in\n",
"      \"mynetwork.net\" with examples from \"test.txt\".  The output mode is\n",
"      \"allactivations\", so the activation generated by each target when\n",
"      presented with each example will be included in SNoW's output.  All\n",
"      results will be written to \"results.txt\".\n",
"\n",
"      snow -evaluate -x \"1, 103, 104(0.67), 109(.95)\" -F mynetwork.net -f-\n",
"\n",
"      This invokes SNoW in evaluation mode, testing the network in\n",
"      \"mynetwork.net\" with the example provided on the command line.  The\n",
"      fixed feature will not be inserted into this example before it is\n",
"      presented to the network.  (By default, it would have been.)\n",
"\n",
#ifdef SERVER_MODE_
"      snow -server 32123 -F mynetwork.net -e count:5\n",
"\n",
"      This invokes SNoW in server mode, listening for clients on port 32123,\n",
"      testing the network in \"mynetwork.net\" with examples provided by the\n",
"      client, and only those features that were active at least five times\n",
"      during training are included in activation calculation.\n",
"\n",
#endif
"&"
};
#endif