trying to understand rationale of unix stream i/o concept


 
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Old 07-11-2009
trying to understand rationale of unix stream i/o concept

I am an entry level programmer with no formal training in computer science. I am trying to enhance my conceptual knowledge about operating systems in general.

I have been using the C programming language on Linux systems for some time and have used the traditional unix stream I/O APIs. The introductory material in books and on the web, typically introduce the unix stream I/O model as "basic unix I/O model is a stream of bytes which can be accessed sequentially or randomly". I understand this part, but I am unable to visualize how else would one access the data. Were the I/O models different before the I/O stream model came in to existence? Are there models in current computing paradigm which are different from the stream I/O. I will appreciate if some can help me visualize both the stream I/O model vs other models (if they exist or existed). Pros-Cons will be a great addition too.

In addition, I came across the following statement on the web (I/O System)

"The basic model of the UNIX I/O system is a sequence of bytes that can be accessed either randomly or sequentially. There are no access methods and no control blocks in a typical UNIX user process."

I do not understand the last statement - what are access methods and control blocks in the context of I/O?

Thanks
Kay
 
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FANN_CASCADETRAIN_ON_DATA(3)						 1					      FANN_CASCADETRAIN_ON_DATA(3)

fann_cascadetrain_on_data - Trains on an entire dataset, for a period of time using the Cascade2 training algorithm

SYNOPSIS
bool fann_cascadetrain_on_data (resource $ann, resource $data, int $max_neurons, int $neurons_between_reports, float $desired_error) DESCRIPTION
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE(3) value should change within fann_get_cascade_output_stagnation_epochs(3) during training of the output connections, in order for the training not to stagnate. If the training stagnates, the training of the output connections will be ended and new candidates will be prepared. This training uses the parameters set using the fann_set_cascade_..., but it also uses another training algorithm as it's internal train- ing algorithm. This algorithm can be set to either FANN_TRAIN_RPROP or FANN_TRAIN_QUICKPROP by fann_set_training_algorithm(3), and the parameters set for these training algorithms will also affect the cascade training. PARAMETERS
o $ann -Neural network resource. o $data -Neural network training data resource. o $max_neurons - The maximum number of neurons to be added to neural network. o $neurons_between_reports - The number of neurons between printing a status report. A value of zero means no reports should be printed. o $desired_error - The desired fann_get_MSE(3) or fann_get_bit_fail(3), depending on which stop function is chosen by fann_set_train_stop_func- tion(3) RETURN VALUES
Returns TRUE on success, or FALSE otherwise. SEE ALSO
fann_train_on_data(3), fann_cascadetrain_on_file(3). PHP Documentation Group FANN_CASCADETRAIN_ON_DATA(3)