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ios.7.1.usr.lib.libCRFSuite.dylib Maven / Gradle / Ivy

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???CQDBCheck for the partition factor... Check for the sequence %d-%d-%d... Check for the marginal probability (0,%d)... Check for the marginal probability (1,%d)... Check for the marginal probability (2,%d)... Check for the marginal probability (0,%d)-(1,%d)... Check for the marginal probability (1,%d)-(2,%d)... OK (%f)
FAIL: %f (%f)
Warning: attributes will not be stored, because "crfsuite_training_do_not_store_attributes" is specified.
Error: missing attribute dictionary.
Warning: labels will not be stored, because "crfsuite_training_do_not_store_labels" is specified.
Error: missing label dictionary.
To use compact or quantized format, you must store attributes and labels out side of crfsuite model.
Storing the model
crf1de_write_model/SourceCache/CRFSuite/CRFSuite-32/crfsuite/lib/crf/src/crf1d_encode.cB == ANumber of active features: %d (%d)
Number of active attributes: %d (%d)
Number of active labels: %d (%d)
Writing labels
Writing attributes
Writing feature references for transitions
Writing feature references for attributes
Seconds required: %.3f

Writing label features
crf1de_write_external_attribute_modelfeature->type == FT_TRANSWriting attribute feature counts and offsets.
w[attribute->fids[j]] != 0Writing attribute features.
Number of attributes: %d
Number of labels: %d
Number of features: %d
Number of active label features: %d
Number of active attribute features: %d
Feature generation
type: CRF1d
feature.minfreq: %f
feature.possible_states: %d
feature.possible_transitions: %d
feature.minfreqThe minimum frequency of features.feature.possible_statesForce to generate possible state features.feature.possible_transitionsForce to generate possible transition features.lCRFFOMCwbLFRFAFRFcrf1dm_initialize_header/SourceCache/CRFSuite/CRFSuite-32/crfsuite/lib/crf/src/crf1d_model.cfalserbSorry, Can't dump trie based model format until we fix traversal bug in CFBurstTrie.
FILEHEADER = {
  magic: %c%c%c%c
  size: %d
  type: %c%c%c%c
  version: %d
  num_features: %d
  num_labels: %d
  num_attrs: %d
  off_features: 0x%X
  off_labels: 0x%X
  off_attrs: 0x%X
  off_labelrefs: 0x%X
  off_attrrefs: 0x%X
}
LABELS = {
  %5d: %s
ATTRIBUTES = {
TRANSITIONS = {
  (%d) %s --> %s: %f
STATE_FEATURES = {
ERROR: This model file already contains an external trie section.cmw_write_attribute_featurefwrite(&feature, 1, sizeof(compact_attribute_feature_t), concrete_writer->fp) == sizeof(compact_attribute_feature_t)qmw_write_label_featuresnum_labels <= 255qmw_write_attribute_feature_counts_and_offsetsfeature_counts[i] <= 255qmw_write_attribute_featuredestination <= 255fwrite(&feature, 1, sizeof(quantized_attribute_feature_t), concrete_writer->fp) == sizeof(quantized_attribute_feature_t)crf1dm_initialize_state_scores_quantizedmodel->quantized_headercrf1dm_initialize_state_scores_compactmodel->compact_headertrain/crf1d/lbfgsl2sgdaveraged-perceptronpassive-aggressivearowERROR: "crfsuite_training_do_not_store_attributes" flag is not compatible with minimal frequency value that is greater than 1!
Holdout group: %d
Performance by label (#match, #model, #ref) (precision, recall, F1):
[UNKNOWN]    %s: (%d, %d, %d) (******, ******, ******)
    %s: (%d, %d, %d) (%1.4f, %1.4f, %1.4f)
Macro-average precision, recall, F1: (%f, %f, %f)
Item accuracy: %d / %d (%1.4f)
Instance accuracy: %d / %d (%1.4f)
dictionaryr%Y-%m-%dT%H:%M:%SZ0%d.intfloatstringunknown%fOperation successfulInvalid argument to functionInsufficient memory to complete operationEntry does not existNo more entries in rangeEntry already existsUNKNOWN ERRORAdaptive Regularization of Weights (AROW)
variance: %f
gamma: %f
max_iterations: %d
epsilon: %f
***** Iteration #%d *****
Loss: %f
Feature norm: %f
Seconds required for this iteration: %.3f
Terminated with the stopping criterion
Total seconds required for training: %.3f
varianceThe initial variance of every feature weight.gammaTradeoff parameter.max_iterationsThe maximum number of iterations.epsilonThe stopping criterion (the mean loss).Averaged perceptron
The stopping criterion (the ratio of incorrect label predictions).c2Coefficient for L2 regularization.The maximum number of iterations (epochs) for SGD optimization.periodThe duration of iterations to test the stopping criterion.deltaThe threshold for the stopping criterion; an optimization process stops when
the improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.calibration.etaThe initial value of learning rate (eta) used for calibration.calibration.rateThe rate of increase/decrease of learning rate for calibration.calibration.samplesThe number of instances used for calibration.calibration.candidatesThe number of candidates of learning rate.calibration.max_trialsThe maximum number of trials of learning rates for calibration.Stochastic Gradient Descent (SGD)
c2: %f
period: %d
delta: %f
***** Epoch #%d *****
ERROR: overflow loss
Improvement ratio: %f
Feature L2-norm: %f
Learning rate (eta): %f
Total number of feature updates: %.0f
SGD terminated with the stopping criteria
SGD terminated with the maximum number of iterations
Calibrating the learning rate (eta)
calibration.eta: %f
calibration.rate: %f
calibration.samples: %d
calibration.candidates: %d
calibration.max_trials: %d
Initial loss: %f
Trial #%d (eta = %f): %f
%f (worse)
Best learning rate (eta): %f
L-BFGS optimization
c1: %f
num_memories: %d
stop: %d
linesearch: %s
linesearch.max_iterations: %d
BacktrackingStrongBacktrackingL-BFGS resulted in convergence
L-BFGS terminated with the stopping criteria
L-BFGS terminated with the maximum number of iterations
L-BFGS terminated with error code (%d)
Error norm: %f
Active features: %d
Line search trials: %d
Line search step: %f
c1Coefficient for L1 regularization.The maximum number of iterations for L-BFGS optimization.num_memoriesThe number of limited memories for approximating the inverse hessian matrix.Epsilon for testing the convergence of the objective.The threshold for the stopping criterion; an L-BFGS iteration stops when the
improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.linesearchMoreThuenteThe line search algorithm used in L-BFGS updates:
{   'MoreThuente': More and Thuente's method,
    'Backtracking': Backtracking method with regular Wolfe condition,
    'StrongBacktracking': Backtracking method with strong Wolfe condition
}
max_linesearchThe maximum number of trials for the line search algorithm.Passive Aggressive
type: %d
c: %f
error_sensitive: %d
averaging: %d
typeThe strategy for updating feature weights: {
    0: PA without slack variables,
    1: PA type I,
    2: PA type II
}.
cThe aggressiveness parameter.error_sensitiveConsider the number of incorrect labels to the cost function.averagingCompute the average of feature weights (similarly to Averaged Perceptron).crf_paragraph_get_id/SourceCache/CRFSuite/CRFSuite-32/crfsuite/compatibility/backward_compatibility.cindex >= 0 && index < paragraph->num_items*paragraph->num_attrscrf_paragraph_set_idindex >= 0Found existing key (%s) for value %d.
Warning: using -h 0 may be faster
*
optimization finished, #iter = %d
warning: class label %d specified in weight is not found
Total nSV = %d
Model doesn't contain information for SVR probability inference
wsvm_type %s
kernel_type %s
degree %d
gamma %g
coef0 %g
nr_class %d
total_sv %d
rho %glabel %dprobAprobBnr_svSV
%.16g 0:%d %d:%.8g %80ssvm_typeunknown svm type.
kernel_typeunknown kernel function.
degree%lfcoef0nr_classtotal_svSVunknown text in model file: [%s]
: 	unknown svm typeunknown kernel typegamma < 0degree of polynomial kernel < 0cache_size <= 0eps <= 0C <= 0nu <= 0 or nu > 1p < 0shrinking != 0 and shrinking != 1probability != 0 and probability != 1one-class SVM probability output not supported yetspecified nu is infeasiblelinearpolynomialrbfsigmoidprecomputedc_svcnu_svcone_classepsilon_svrnu_svrExceeds max_iter in multiclass_prob
Line search fails in two-class probability estimates
Reaching maximal iterations in two-class probability estimates
obj = %f, rho = %f
nSV = %d, nBSV = %d
epsilon = %f
nu = %f
C = %f
Prob. model for test data: target value = predicted value + z,
z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g

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w[attribute->fids[j]] != 0Writing attribute features.
Number of attributes: %d
Number of labels: %d
Number of features: %d
Number of active label features: %d
Number of active attribute features: %d
Feature generation
type: CRF1d
feature.minfreq: %f
feature.possible_states: %d
feature.possible_transitions: %d
feature.minfreqThe minimum frequency of features.feature.possible_statesForce to generate possible state features.feature.possible_transitionsForce to generate possible transition features.lCRFFOMCwbLFRFAFRFcrf1dm_initialize_header/SourceCache/CRFSuite/CRFSuite-32/crfsuite/lib/crf/src/crf1d_model.cfalserbSorry, Can't dump trie based model format until we fix traversal bug in CFBurstTrie.
FILEHEADER = {
  magic: %c%c%c%c
  size: %d
  type: %c%c%c%c
  version: %d
  num_features: %d
  num_labels: %d
  num_attrs: %d
  off_features: 0x%X
  off_labels: 0x%X
  off_attrs: 0x%X
  off_labelrefs: 0x%X
  off_attrrefs: 0x%X
}
LABELS = {
  %5d: %s
ATTRIBUTES = {
TRANSITIONS = {
  (%d) %s --> %s: %f
STATE_FEATURES = {
ERROR: This model file already contains an external trie section.cmw_write_attribute_featurefwrite(&feature, 1, sizeof(compact_attribute_feature_t), concrete_writer->fp) == sizeof(compact_attribute_feature_t)qmw_write_label_featuresnum_labels <= 255qmw_write_attribute_feature_counts_and_offsetsfeature_counts[i] <= 255qmw_write_attribute_featuredestination <= 255fwrite(&feature, 1, sizeof(quantized_attribute_feature_t), concrete_writer->fp) == sizeof(quantized_attribute_feature_t)crf1dm_initialize_state_scores_quantizedmodel->quantized_headercrf1dm_initialize_state_scores_compactmodel->compact_headertrain/crf1d/lbfgsl2sgdaveraged-perceptronpassive-aggressivearowERROR: "crfsuite_training_do_not_store_attributes" flag is not compatible with minimal frequency value that is greater than 1!
Holdout group: %d
Performance by label (#match, #model, #ref) (precision, recall, F1):
[UNKNOWN]    %s: (%d, %d, %d) (******, ******, ******)
    %s: (%d, %d, %d) (%1.4f, %1.4f, %1.4f)
Macro-average precision, recall, F1: (%f, %f, %f)
Item accuracy: %d / %d (%1.4f)
Instance accuracy: %d / %d (%1.4f)
dictionaryr%Y-%m-%dT%H:%M:%SZ0%d.intfloatstringunknown%fOperation successfulInvalid argument to functionInsufficient memory to complete operationEntry does not existNo more entries in rangeEntry already existsUNKNOWN ERRORAdaptive Regularization of Weights (AROW)
variance: %f
gamma: %f
max_iterations: %d
epsilon: %f
***** Iteration #%d *****
Loss: %f
Feature norm: %f
Seconds required for this iteration: %.3f
Terminated with the stopping criterion
Total seconds required for training: %.3f
varianceThe initial variance of every feature weight.gammaTradeoff parameter.max_iterationsThe maximum number of iterations.epsilonThe stopping criterion (the mean loss).Averaged perceptron
The stopping criterion (the ratio of incorrect label predictions).c2Coefficient for L2 regularization.The maximum number of iterations (epochs) for SGD optimization.periodThe duration of iterations to test the stopping criterion.deltaThe threshold for the stopping criterion; an optimization process stops when
the improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.calibration.etaThe initial value of learning rate (eta) used for calibration.calibration.rateThe rate of increase/decrease of learning rate for calibration.calibration.samplesThe number of instances used for calibration.calibration.candidatesThe number of candidates of learning rate.calibration.max_trialsThe maximum number of trials of learning rates for calibration.Stochastic Gradient Descent (SGD)
c2: %f
period: %d
delta: %f
***** Epoch #%d *****
ERROR: overflow loss
Improvement ratio: %f
Feature L2-norm: %f
Learning rate (eta): %f
Total number of feature updates: %.0f
SGD terminated with the stopping criteria
SGD terminated with the maximum number of iterations
Calibrating the learning rate (eta)
calibration.eta: %f
calibration.rate: %f
calibration.samples: %d
calibration.candidates: %d
calibration.max_trials: %d
Initial loss: %f
Trial #%d (eta = %f): %f
%f (worse)
Best learning rate (eta): %f
L-BFGS optimization
c1: %f
num_memories: %d
stop: %d
linesearch: %s
linesearch.max_iterations: %d
BacktrackingStrongBacktrackingL-BFGS resulted in convergence
L-BFGS terminated with the stopping criteria
L-BFGS terminated with the maximum number of iterations
L-BFGS terminated with error code (%d)
Error norm: %f
Active features: %d
Line search trials: %d
Line search step: %f
c1Coefficient for L1 regularization.The maximum number of iterations for L-BFGS optimization.num_memoriesThe number of limited memories for approximating the inverse hessian matrix.Epsilon for testing the convergence of the objective.The threshold for the stopping criterion; an L-BFGS iteration stops when the
improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.linesearchMoreThuenteThe line search algorithm used in L-BFGS updates:
{   'MoreThuente': More and Thuente's method,
    'Backtracking': Backtracking method with regular Wolfe condition,
    'StrongBacktracking': Backtracking method with strong Wolfe condition
}
max_linesearchThe maximum number of trials for the line search algorithm.Passive Aggressive
type: %d
c: %f
error_sensitive: %d
averaging: %d
typeThe strategy for updating feature weights: {
    0: PA without slack variables,
    1: PA type I,
    2: PA type II
}.
cThe aggressiveness parameter.error_sensitiveConsider the number of incorrect labels to the cost function.averagingCompute the average of feature weights (similarly to Averaged Perceptron).crf_paragraph_get_id/SourceCache/CRFSuite/CRFSuite-32/crfsuite/compatibility/backward_compatibility.cindex >= 0 && index < paragraph->num_items*paragraph->num_attrscrf_paragraph_set_idindex >= 0Found existing key (%s) for value %d.
Warning: using -h 0 may be faster
*
optimization finished, #iter = %d
warning: class label %d specified in weight is not found
Total nSV = %d
Model doesn't contain information for SVR probability inference
wsvm_type %s
kernel_type %s
degree %d
gamma %g
coef0 %g
nr_class %d
total_sv %d
rho %glabel %dprobAprobBnr_svSV
%.16g 0:%d %d:%.8g %80ssvm_typeunknown svm type.
kernel_typeunknown kernel function.
degree%lfcoef0nr_classtotal_svSVunknown text in model file: [%s]
: 	unknown svm typeunknown kernel typegamma < 0degree of polynomial kernel < 0cache_size <= 0eps <= 0C <= 0nu <= 0 or nu > 1p < 0shrinking != 0 and shrinking != 1probability != 0 and probability != 1one-class SVM probability output not supported yetspecified nu is infeasiblelinearpolynomialrbfsigmoidprecomputedc_svcnu_svcone_classepsilon_svrnu_svrExceeds max_iter in multiclass_prob
Line search fails in two-class probability estimates
Reaching maximal iterations in two-class probability estimates
obj = %f, rho = %f
nSV = %d, nBSV = %d
epsilon = %f
nu = %f
C = %f
Prob. model for test data: target value = predicted value + z,
z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g
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