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Cannot open directory %s ...Found %d files Number of classes invalid! Exitus... Loading ... %cCannot open %s! Exitus... rrCannot open %s! Exitus... Scanning file: %s %s%d%dDim. of feature vector inconsistent: %d != %d! Exitus.. %dWarning file %s has only %d samples! Allocation problem with 'U->C[i].S' - exitus... Allocation problem with 'U->C[i].mean' - exitus... Allocation problem with 'U->C[i].stddev' - exitus... Allocation problem with 'U->C[i].sqrsum' - exitus... %fWARNING: Standard deviation is zero for class %d and feature %d This might cause some trouble. Continue (y/n)?y...Exitus... A priori probability of class %d=%5.2f%% Cannot open %s!...rrCannot open %s!... Scanning file: %s %d%f%sFound an empty class name. Exit... Number of classes invalid! Exitus... Allocation problem with 'U->C[i].S' - exitus... Allocation problem with 'U->C[i].mean' - exitus... Allocation problem with 'U->C[i].stddev' - exitus... Allocation problem with 'U->C[i].sqrsum' - exitus... rrCannot open %s! Exitus... %fLoading ... %c%f%sloadUnivFile> Cannot find class %s Exitus... WARNING: Standard deviation is zero for class %d and feature %d This might cause some trouble. Continue (y/n)?y...Exitus... A priori probability of class %d=%5.2f%% loadUnivFile> Inconsistent data Exitus... U->C[%d].numSampl=%d != samplesPerClass[%d]=%d Load data from (f)ile or from (d)irectory? Load data from file? Load data from directory? Unknown option - nothing done...hԥ?@YAllocation problem with 'Xi' - exitus... Allocation problem with 'Xd' - exitus... Allocation problem with 'Psi' - exitus... Allocation problem with 'auxFeatSet' - exitus... Could not find element - exit... Leaf level=%d --- Actual level=%d Allocation problem with 'Q_seq' - exitus... BOUND = %f --- Best selected set so far: Number of features to be selected from the %d possible: Branch and bound - PACIENCIA - PATIENCE - PACIENCE - GEDULD! ... STATISTICS OF THE SEARCH PROCESS: Number of leafs visited = %.0lf of %.0lf possible = %5.2lf%% Allocation problem with 'U->FSV' - exitus... ?@hԥ?For feature %d the two classes %d and %d have the same mean %f Setting the criterion to -INFINITY... Unknown criterion- exit Allocation problem with ' FSV ' - exitus... Analyzing feature nr. %4d of %4d Sorting... done. Want to see the best ? features (1-%d): Want to select the best ? features (1-%d): @hԥ?For feature %d the two classes %d and %d have the same mean %f Setting the criterion to -INFINITY... Selection criterion[%d] = %7.3f Name: %s Sorting... done. Want to see the best ? features (1-%d): Invalid value! Again ? Nr. %4d = %4d --- Criterion = %7.5f Name: %s Want to select the best ? features (1-%d): Invalid value! Again ? --- SELECTED FEATURES --- : %d Nr. %4d = %4d --- Criterion = %7.5f Name: %s Shԥhԥ?@$./_correl_wCannot open %s! Exitus... Dumping %s %f %f ./_tmp.gnuwCannot open %s! Exitus... Dumping %s # # Batch file to visualize linear correlation # Generated automatically ! # # Universe %s set title "LINEAR CORRELATION FOR CLASSES %d " set xlabel "First feature = %d" set ylabel "Second feature = %d" set xrange [%f:%f] set yrange [%f:%f] plot "_correl_" pause -1 "Hit return to exit..." cd %s %sgnuplot %s./exec --- Execute: %s corr> Not enough samples: %d; Exit... corr> Cannot calculate correlation coefficient because standard deviation is 0; Exit... >>>----------------------------------------------------<<< RESULT OF LINEAR CORRELATION ANALYSIS Feature nr 1: %d Mean=%7.3f Standard deviation=%7.3f Feature nr 2: %d Mean=%7.3f Standard deviation=%7.3f Covariance=%7.3f ===> Correlation coefficient=%7.3f <=== >>>----------------------------------------------------<<< >>>--- Correlation analysis between two features ---<<< First feature? Invalid value! Again ? Second feature? Invalid value! Again ? Correlation of all classes (y/n)?yNumber of classes to be analyzed? Invalid value! Again ? Class nr.%d ? Invalid value! Again ? Program interrupted : (M)ain menu , (Q)uit without saving: --- LOADING: --- ERROR ESTIMATION: --- SAMMON PLOT: --- LEARNING WITH Q*: >>>>>---------------------------------------------------<<<<<< >>>>> <<<<<< >>>>> TOOLDIAG <<<<<< >>>>> Pattern recognition package <<<<<< >>>>> Copyright (C) 1992, 1993, 1994 Thomas W. Rauber <<<<<< >>>>> Universidade Nova de Lisboa & <<<<<< >>>>> UNINOVA - Intelligent Robotics Center <<<<<< >>>>> E-Mail: tr@fct.unl.pt <<<<<< >>>>> (Bug reports and comments welcome) <<<<<< >>>>> <<<<<< >>>>>---------------------------------------------------<<<<<< --- Type '?' followed by the menu choice for help --- (1) Load universe from directory or file --- Data is normalized to [0,1] (2) Normalize data to [0,1] (3) Feature selection (4) Feature extraction (5) Learning with Q* - algorithm (6) Error estimation (7) Identify from independent data (8) Sammon plot (9) Statistical analysis (10) Interfacing (11) Batch demo (Q)uit Choice: @?@??ffffff??????<@@hԥ@@>z򚼯H?zG{?Numerical Recipes run-time error... %s ...now exiting to system... Peeping matrix... %7.5f %7.20f Too many iterations in HQRAllocation problem with ' indxc ' - exitus... Allocation problem with ' indxr ' - exitus... Allocation problem with ' ipiv ' - exitus... GAUSSJ: Singular Matrix-1GAUSSJ: Singular Matrix-2Singular matrix in routine LUDCMPAllocation problem with 'bVec' - exitus... Allocation problem with 'indx' - exitus... Allocation problem with 'xi' - exitus... Allocation problem with 'xi1' - exitus... Allocation problem with 'y' - exitus... Allocation problem with 'y_init_max' - exitus... Had trouble calculating eigenvector nr. %d Error in norm_vector: vector has 0 length - exit ... Had a zero SW scatter matrix - exit...What is Euclidean distance %d? - exit... _Linear discriminant analysisFeature extractor is empty... ***** %d x %d FEATURE EXTRACTOR: ***** ...Feature extractor is empty..../Saving linear feature extractor to: %swCannot open %s! Exitus... # Universe: %s # Row and column dimension of the linear feature extractor %d %d %20.15f Nothing saved... rCannot open %s! nothing done... Loading feature extractor from %sWarning universe names different: %s --- %s %d %d ERROR: Row size(%d) of the extractor is different from nr. of features(%d) Returning...%lf./Loading linear feature extractor from: %sFeature extractor is empty..../Saving the extracted data in LVQ format in file: %sNothing saved... wCannot open %s! Exitus... Generating LVQ-File: %s %d %f %s ...done. Do you want to save the extracted samples (y/n)? y >>>>>----- FEATURE EXTRACTION MENU -----<<<<<< --------------- Method ---------------- (%d) %s --------------- Tools ----------------- (S)ave linear feature extractor matrix to file (L)oad linear feature extractor matrix from file S(H)ow matrix (E)xtract samples in memory with actual extractor (Q)uit Choice: %d Please load universe first !...f(fEfbffffff fgg$g;gMgXgmg|gg ggh?@YScatter matrices distance J1Scatter matrices distance J2Scatter matrices distance J3Scatter matrices distance J4MinkowskiCity blockEUCLIDEAN DISTANCEChebychevNonlinear (Parzen & hyperspheric kernel)Estimated minimal error probabilityChernoffBhattacharyya distanceMatusita distanceDivergenceMahalanobis distancePatrick-FisherINTER-CLASS-DISTANCEUnivariate Chebychev = 1 - (s1+s2)^2 / (m1-m2)^2Best FeaturesSequential Forward SearchSequential Backward SearchBranch and Bound ### Specify selection criterion for strategy %d = %s ### (%d) %s Choice: Enter parameter of Chernoff probabilistic distance [0,1]: --- Specify Euclidan distance metric --- (%d) %s Choice: ### Specify distance metric for strategy %d = %s ### and criterion = %s (%d) %s Choice: Order of Minkowski metric: Calculate Euclidean distance from scatter matrices i.e. criteria J1 to J4? (y/n)nCalculate distance only between different classes (1) or also within the same class itself(2)?2 --------------------------------------------------------------- Selection strategy: %s criterion: %s with s = %.3f distance metric: %s of order s=%f based on criterion: %s Calculating mutual distance between ALL classes Calculating mutual distance only between DIFFERENT classes --------------------------------------------------------------- Is this what you want? (y/n)y --- FEATURE SELECTION: Search strategy = Sequential forward Selection criterion = Euclidean distance Distance is calculated between all classes. >>>>>----- FEATURE SELECTION MENU -----<<<<<< ----------- Search strategy ---------------- (%d) %s (T)ools and old algorithms (Q)uit Choice: %dWhat the hell is crit %d? - exit... Please load universe first !...#%d: { %d } --- SELECTED FEATURES --- : %d Nr. %4d = %4d --- Criterion = %7.5f Name: %s ./Saving selected feature mask to: %swCannot open %s! Exitus... %s %d # Were the feature values normalized to [0,1] during selection ? normalized unnormalized %d %f %s Nothing saved... rCannot open %s! nothing done... Loading selected features from %s Warning universe names different: %s --- %s %d Too much selected features (%d > %d) in %s Features were selected from normalized data. => Feature values will be normalized... Error allocating 'U->FSV' --- SELECTED FEATURES = %d --- %d %fNr. %4d = %4d --- Criterion = %7.5f Name: %s ./Load selected feature mask from: %srCannot open %s! nothing done... Loading feature description file from %s %d Nr. of feature names unequal nr. of features (%d != %d) in %s %s --- FEATURE NAMES --- Nr. %5d: %s No features are selected... to continue... >>>>>----- FEATURE SELECTION TOOLS (+old algorithms) ---<<<<<< (1) Load selected features from file (2) Set all features as selected (3) Show all selected features (4) Save all selected features to a file Selection criterion | Search algorithm ------------------------------------------------------------- (7) M-COVAR | (SFS - Sequential Forward Search) (8) UNIVAR | (BF - Best Features) (9) MINERR | (SFS - Sequential Forward Search) (Q)uit Choice: Please load universe first !... help> a) Load the universe from a directory. A directory will be scanned for files which contain the samples for one class. The directory may contain only the data files. The format of one data file is as follows: CLASS_NAME DIMENSION NUMBER_OF_SAMPLES sample1 sample2 ... sampleN b) Load the universe from a file. The file must have the follwing format: DIMENSION ... CLASS_NAME ... ... CLASS_NAME Execute a sequence of commands for demo purpose. Generate input data files for other packages. Normalize each feature individually. 1.) Detect the maximum and minimum of all values for all classes of that feature. 2.) Transform each value x to (x-min)/(max-min). Feature selection and extraction. Consult the book: Devijver, P.A., and Kittler, J.,"Pattern Recognition -- A Statistical Approach," Prentice/Hall Int., London, 1982 Generate a set of representative prototypes from the set of all samples by supervised clustering. Use leave-out-out error estimation method. Use a simple nearest neighbor classifier to identify data from a file. The test data should be independent from the training data. Generate a 2-dimensional plot of the data. The multidimensional sample space which is normally higher than 2 dimensions is mapped to a plane. The selected features are used. Do some statistics with the data. Ends the program. Interface to the LVQ_PAK program. Interface to the SNNS program. Merge two data files column by column, Useful for integration of several feature groups. From the actual sample set generate a random split. Ends the program. Generate a input data file for the LVQ_PAK program. Open a file, read samples and write new file that only contains the selected features (LVQ_PAK file format). Ends the program. Generate a network and pattern file for the SNNS program. Open a file, read samples and write new pattern file that only contains the selected features. Use the format of the the data input files to TOOLDIAG. Ends the program. Calculate and plot linear correlation between 2 features. Ends the program. help> unknown loop mode... ... to continue...@Y?What is classifier %d? -exit... #### Using Nearest-Neighbor Classifier #### What is classifier %d? -exit... Load universe or select features first...Load benchmark data from file? rrCannot open %s!... %dUniverse and file have different feature dimensions: %d != %d...%f%sFound an empty class name. Exit... Actual error = %7.2f%% --- RESULT for benchmark file %s ERROR RATE = %6.2f%% --- ACCURACY %6.2f%% --- Recognized %d of %d _train_testB@Y --- Merging two data files --- Merge file: File name empty...rrFile not found...Error in merge_data_files, exit... and file: File name empty...File names identical...rrFile not found...Error in merge_data_files, exit... to file: File name empty...File names identical...File names identical... Write %s in b)inary or a)scii mode ?bwwCannot open %s! Exitus... Merging %s and %s to %s %d%d%d %f%f%s%s%f %f %s %s ERROR: Detected different class names in files %s and %s >>>%s<<< ---- >>>%s<<< ... --- Splitting the actual data set into training and test --- Training file name: %s././ Test file name: %sFile names identical, using default. ./ Write in b)inary or a)scii mode ?bwwwwCannot open %s! Cannot open %s! Exitus... Split data into ? percent training data? 70%fInvalid value! Again ? Splitting samples into %s and %s %d %d Sample neither train nor test. Exit... %f %s >>>>>----- INTERFACE MENU -----<<<<<< (1) Learning Vector Quantization (LVQ) (2) Stuttgart Neural Network Simulator (SNNS) (3) Merge two data files to a single data file (4) Split a data file randomly into two (train & test) (Q)uit Choice: Please load universe first !...??PbMhԥAllocation problem with 'wr' - exitus... Allocation problem with 'wi' - exitus... Allocation problem with 'sortEigVal' - exitus... Allocation problem with 'eigVec' - exitus... Inverting within classes scatter matrix... done. Multiplying matrices... done. Calculating eigenvalues... Calculating eigenvalue nr. %d Eigenvalue %d had some imaginary part: %.5f N O N - Z E R O E I G E N V A L U E S : none Sorry, this data seems inappropriate for feature extraction... Eigenvalue nr. %3d of %d = %15.6f - DETERMINATION OF DIMENSION OF THE REDUCED FEATURE SPACE - As the minimum of the original dimension of the feature space(%d) and the number of classes minus one(%d) is %d, and since the number of non-zero eigenvalues was %d, choose a value for the reduced dimension between %d and %d: Calculating eigenvector for eigenvalue nr. %d New values from the inverse iteration procedure: Eigenvalue nr. %3d of %d = %15.6f Allocation problem with 'FSV' - exitus... --- FEATURE EXTRACTION BASED ON DISCRIMINANT ANALYSIS --- Calculating %d x %d between and within classes scatter matrices... done. Shԥhԥ?@$=yd-/??ə Select features first please!..../Saving the data in LVQ format in file: %swCannot open %s! Exitus... Generating LVQ-File: %s %d %f %s ./Saving the feature names in file: %swCannot open %s! Exitus... Generating feature description-File: %s %d %s Select features first please!...Name of the file to be filtered? r Cannot open %s..../Name of the output file with filtered features? %s Sorry file names are the same!...w Cannot open %s...Filtering %s TO %s %dUniverse and file have different feature dimensions: %d != %d...%d %f%s%f %s ./wCannot open %s! Exitus... Dumping %s # %s %f %f ./_tmp.gnuwCannot open %s! Exitus... Dumping %s # # Batch file to visualize sammon plot # Generated automatically ! # # Universe %s set title "SAMMON PLOT" set xrange [%f:%f] set yrange [%f:%f] plot "%s", "%s" pause -1 "Hit return to exit..."cd %s %sgnuplot %s./exec --- Execute: %s Please load universe first !... Select features first please!...It does not make sense to map unidimensional data to n dimensions!...No space for buffer 'Samples'! Exitus... Number of selected features is 2 - Mapping the data directly to 2 dimensions, without using the Sammon mapping. Sammon: Number of iterations (default=%d): %dValue %d for iterations is invalid. Setting to default %d...Warning: distance between samples %d and %d is 0 Setting distance to a minimum %e iterations to go: %6d >>>>>----- Learning Vector Quantization (LVQ) -----<<<<<< (1) Generate data file (2) Filter only selected features from a file (Q)uit Choice: hԥ@YNo space for buffer 'F_Ratios' No space for buffer 'FSV' Selecting feature Nr. %d ... %3d%% Predictor %d had maximum F-Ratio: %7.3f - F_Ratio_Thresh: %7.3f Substituting first feature by second !... Available features: %d --- SELECTED FEATURES --- : %d Nr. %3d = %3d F-Ratio: %f Name: %s >>>--- Selecting features ---<<< Maximum number of features to be selected (1-%d)? Invalid value! Again ? Selected %d features. ?? %7.5f Warning: trying allocate space for an empty matrix!!! Warning: trying allocate space for a non-empty pointer! No memory allocated for matrix with dimensions %d x %d ...Add_Matrix> Cannot add matrices !!!...Subtract_Matrix> Cannot subtract matrices !!!...Mult_Matrix> Cannot multiply matrices !!!...Split_Matrix> problems !!!...Invert_Matrix> not square matrix !. Exit... trace> not square matrix !. Exit... %lfRows= Colums= M[%d][%d]= Shԥ?Shԥ?@$@YB@Y??@Y??Nearest neighbor K=%d%dValue %d for K is invalid. Setting to default %d... --------- Classification result ------------- %3d. Neighbor = %20s with distance %.6f No space for buffer 'Sample'.... Exitus... No space for buffer 'Sample'. Exitus... No space for buffer 'FV'. Exitus... No space for buffer 'NN'. Exitus... # Features: %2d - K: %2d - Total number of samples: %7d Samples to leave out:Warning!: Class %d = %s has less samples than K = %d...%7d --- Confusion Matrix --- No space for buffer 'NN'. Exitus... Unknown class: "%s" - Exitus... Confused %s with expected class %s ./_error_wCannot open %s! Exitus... Dumping %s %3d %7.5f ./_tmp.gnuwCannot open %s! Exitus... Dumping %s # # Batch file to visualize error estimate # Generated automatically ! # # Universe %s set xrange [1:%d] set xrange [0.9:1.1] set xtics 0,1,%d set yrange [%d:%d] set title "ERROR ESTIMATION: LEAVE-ONE-OUT" set xlabel "Number of selected features" set ylabel "Estimated error [%%]" plot "_error_" with linespoints pause -1 "Hit return to exit..." cd %s %sgnuplot %s./exec --- Execute: %s Select features first..../Saving the error estimation protocol to file: %swCannot open %s! Exitus... Universe: %s Nothing saved... --- Show confusion matrix (y/n)?n- Estimating error using leave-one-out ---> Error estimate = %5.2f%% using %4d of %4d features Nr. Features: %3d - Estimated error: %5.2f %% - Accuracy: %5.2f %% ./Saving the error estimation protocol to file: %swCannot open %s! Exitus... Universe: %s Nothing saved... Maximum number of features to be selected (1-%d)? --- Show confusion matrix (y/n)?nConsidering feature nr. %4d of %4d-%4d as candidate nr. %4d ---> Error estimate = %5.2f %% For feature vector: ( %d ) Nr. Features: %3d - Estimated error: %5.2f %% - Accuracy: %5.2f %% --- SELECTED FEATURES --- : %d Nr. %3d = %3d --- Criterion = %7.5f Name: %s ?Data already normalized...Load first...Feature %d has the same min and max value %f Do you wish to continue (y/n)?y...Exitus... normalize> illegal value=%f; Exitus... @S vSUW+֧@84~?S&M[e!1?@?@ %?????PH硽?.B9@Parameter for the calculus of the radius: eta=%2.1f%fValue %f for eta is invalid. Setting to default %f... Using Parzen estimator with hyperspheric kernel with eta=%f! Initializing ... Allocation problem with ' hypersphere_kernel_param ' - exitus... done. |/-\Shԥ@Shԥ@<VShԥ@YShԥ ---- Update rule for the prototype ---- Update as the MEAN(1), MARGINAL MEDIAN(2) or VECTOR MEDIAN(3) of the correctly classified neighbors? %d Initialize first prototype of each class randomly (y/n)?n Inializing first prototype conforming update rule. Allocation problem with ' newFriends ' - exitus... Prototype has %d friends: ( %d ) Allocation problem with ' PTdest->friends ' - exitus... Allocation problem with ' Samples ' - exitus... S A M P L E S: Class: %d = %s - Nr. Samples: %d Nr. %3d -------------- P R O T O T Y P E S ------------------- %4d prototypes for class: %2d = %s Nr. %3d: Outlier! Performance index = %20.10f ------------------------------------------------------- --- Purging outliers --- %4d prototypes for class: %2d = %s purge_outliers> No space! Exitus... Prototype Nr. %3d: *** Found %d outliers for class %d purge_outliers> No space for PurgedPT! Exitus... Allocation problem with ' EuclidDistsSum ' - exitus... Allocation problem with ' seq ' - exitus... Allocation problem with ' newPTvalue ' - exitus... Trouble with update method - exit... %d-%d Write all prototypes to a file (y/n)? y Write file in LVQ format (y/n)? ySave prototypes to file: %s./Nothing saved... ././.symwCould not open %s for writing !!! # # Prototype file for universe: %s # # Number of classes: %d # Dimension of continuous feat. vector: %d # Number of prototypes for all classes: %d # # %s # %7.5f %d-%d # outlier %s # # The discretized samples # # ClassNr-PrototypeNr ClassName # %s %s # # There were %d of %d = %6.3f%% samples misclassified # Warning: offset is %d Try changing the 'RANGE' in 'util.c' init_prototypes> Setting to 0 Allocation problem with ' allPT.friends ' - exitus... Trouble with update method - exit... CLUSTER: Verbose (y/n)? n*** Ignore ouliers because of initialization *** Learning prototypes... %c +++++ END OF RUN %d +++++ --- F I N A L R E S U L T after %d runs: --- --- Sucessfully clustered in %d runs --- CLUSTER: Purge all single sample prototypes (y/n)? y --- R E S U L T A F T E R P U R G E: ---Select features first...Could not find the argument for parameter %s - exit... Can't find asked option %s%s Can't find asked option %s%s usage: %s [-dir | -file ] [-sel ] [-v] [-fnam ] -v-dir-fileChoose only one option -file or -dir !; Exitus... Loading data files... -fnam-selUnknown option "%s" or missing parameter - Exit... hԥReturn logaritm of selection criterion instead criterion? (y/n)nAllocation problem with ' FSV ' - exitus... Allocation problem with ' already_deleted ' - exitus... How many features do you wish to select ? (1-%d): Feature nr. %4d of %4d-%4d is candidate nr. %4d for deletion Error when selecting; best criterion is invalid! Exitus... Allocation problem with ' U->FSV ' - exitus... ?????????@@@!TD-??@?Invalid argument %lf %lf- Exit... Invalid argument %lf %lf- Exit... What the hell is crit %d? - exit... Had some zero element in the covariance matrix of class %d - Exitus... What the hell is crit %d? - exit... hԥm?@Problems in dist_CHEBYCHEV -exit... Unknown metrik - exit Unknown metrik - exit What the hell is crit %d? - exit... Unknown metrik - exit What the hell is crit %d? - exit... hԥReturn logaritm of selection criterion instead criterion? (y/n)nAllocation problem - exit... Allocation problem - exit... How many features do you wish to select ? (1-%d): Feature nr. %4d of %4d-%4d is candidate nr. %4d Criterion=%.4f SNNS network definition file V1.4-3D generated at %s network name : TOOLDIAG source files : no. of units : %d no. of connections : %d no. of unit types : %d no. of site types : %d learning function : Std_Backpropagation update function : Topological_Order unit definition section : no. | typeName | unitName | act | bias | st | position | act func | out func | sites -------|----------|-----------------|----------|----------|----|----------|----------|----------|------- %5d | | %15s | %f | %f | i | %d, %d, 0 ||| %5d | | | %f | %f | h | %d, %d, 0 ||| %5d | | %15s | %f | %f | o | %d, %d, 0 ||| -------|----------|-----------------|----------|----------|----|----------|----------|----------|------- connection definition section : target | site | source:weight -------|------|--------------------------------------------- %5d | | %d: 0.00000, %d: 0.00000 %5d | | %d: 0.00000, %d: 0.00000 -------|------|--------------------------------------------- SNNS pattern definition file V1.4 generated at %s No. of patterns : %d No. of input units : %d No. of output units : %d # # Pattern file for the universe: %s # # # %s # %f 1 0 Select features first please!..../Saving the net and pattern file in (without extension): %s.net.patwCannot open %s! Exitus... wCannot open %s! Exitus... Generating Network File: %s Generating Pattern File: %s Select features first please!...Name of the file to be filtered to test file? r Cannot open %s..../Name of the output file with filtered features? %s Sorry file names are the same!....patw Cannot open %s...Filtering %s TO %s %dUniverse and file have different feature dimensions: %d != %d...%f%srSNNS pattern definition file V1.4 generated at %s No. of patterns : %d No. of input units : %d No. of output units : %d # # Test pattern file for the universe: %s # %f%s# Pattern nr. %d - class=%s %f ERROR: Unknown class %s at line %d - exitus... 1 0 >>>>>----- Stuttgart Neural Network Simulator (SNNS) -----<<<<<< (1) Generate network and training pattern file (2) Filter only selected features from a file and generate test pattern file (Q)uit Choice: >>>>>----- STATISTICAL ANALYSIS -----<<<<<< (1) Correlation between 2 features (Q)uit Choice: Please load universe or select features first !...@j%s %s - CPU Sec*CLOCKS_PER_SEC= %ld CPU Sec.=%.3f @@??@Y@Y??r ( %.5f ) %s %d of %d train: %d test: %d Sample type error; exit... 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