Berkeley DB 4.7, 4.8, 5.1, 5.3, 6.1 and 6.2 Python Extension Package¶
Introduction¶
This is a simple bit of documentation for the bsddb3.db Python extension module which wraps the Berkeley DB C library. The extension module is located in a Python package along with a few pure python modules.
It is expected that this module will be used in the following general ways by different programmers in different situations. The goals of this module are to allow all of these methods without making things too complex for the simple cases, and without leaving out funtionality needed by the complex cases.
Backwards compatibility: It is desirable for this package to be a near drop-in replacement for the bsddb module shipped with Python which is designed to wrap either DB 1.85, or the 1.85 compatibility interface. This means that there will need to be equivalent object creation functions available, (btopen(), hashopen(), and rnopen()) and the objects returned will need to have the same or at least similar methods available, (specifically, first(), last(), next(), and prev() will need to be available without the user needing to explicitly use a cursor.) All of these have been implemented in Python code in the bsddb3.__init__.py module.
Simple persistent dictionary: One small step beyond the above. The programmer may be aware of and use the new DB object type directly, but only needs it from a single process and thread. The programmer should not have to be bothered with using a DBEnv, and the DB object should behave as much like a dictionary as possible.
Concurrent access dictionaries: This refers to the ability to simultaneously have one writer and multiple readers of a DB (either in multiple threads or processes) and is implemented simply by creating a DBEnv with certain flags. No extra work is required to allow this access mode in bsddb3.
Advanced transactional data store: This mode of use is where the full capabilities of the Berkeley DB library are called into action. The programmer will probably not use the dictionary access methods as much as the regular methods of the DB object, so he can pass transaction objects to the methods. Again, most of this advanced functionality is activated simply by opening a DBEnv with the proper flags, and also by using transactions and being aware of and reacting to deadlock exceptions, etc.
Types Provided¶
The bsddb3.db extension module provides the following object types:
DB: The basic database object, capable of Hash, BTree, Recno, and Queue access methods.
DBEnv: Provides a Database Environment for more advanced database use. Apps using transactions, logging, concurrent access, etc. will need to have an environment object.
DBCursor: A pointer-like object used to traverse a database.
DBTxn: A database transaction. Allows for multi-file commit, abort and checkpoint of database modifications.
DBLock: An opaque handle for a lock. See DBEnv.lock_get() and DBEnv.lock_put(). Locks are not necessarily associated with anything in the database, but can be used for any syncronization task across all threads and processes that have the DBEnv open.
DBSequence: Sequences provide an arbitrary number of persistent objects that return an increasing or decreasing sequence of integers. Opening a sequence handle associates it with a record in a database.
DBSite: Site object for Replication Manager.
Top level functions¶
-
version
()¶ Returns a tuple with major, minor and patch level. More info…
-
full_version
()¶ Returns a tuple with the full version string, family, release, major, minor and patch level. More info…
Exceptions Provided¶
The Berkeley DB C API uses function return codes to signal various errors. The bsddb3.db module checks for these error codes and turns them into Python exceptions, allowing you to use familiar try:… except:… constructs and not have to bother with checking every method’s return value.
Each of the error codes is turned into an exception specific to that error code, as outlined in the table below. If you are using the C API documentation then it is very easy to map the error return codes specified there to the name of the Python exception that will be raised. Simply refer to the table below.
Each exception derives from the DBError exception class so if you just want to catch generic errors you can use DBError to do it. Since DBNotFoundError is raised when a given key is not found in the database, DBNotFoundError also derives from the standard KeyError exception to help make a DB look and act like a dictionary. We do the same trick with DBKeyEmptyError.
When any of these exceptions is raised, the associated value is a tuple containing an integer representing the error code and a string for the error message itself.
DBError
Base class, all others derive from this
DBCursorClosedError
When trying to use a closed cursor
DBForeignConflictError
DB_FOREIGN_CONFLICT
DBKeyEmptyError
DB_KEYEMPTY (also derives from KeyError)
DBKeyExistError
DB_KEYEXIST
DBLockDeadlockError
DB_LOCK_DEADLOCK
DBLockNotGrantedError
DB_LOCK_NOTGRANTED
DBNotFoundError
DB_NOTFOUND (also derives from KeyError)
DBOldVersionError
DB_OLD_VERSION
DBPageNotFoundError
DB_PAGE_NOTFOUND
DBRepHandleDeadError
DB_REP_HANDLE_DEAD
DBRepLeaseExpiredError
DB_REP_LEASE_EXPIRED
DBRepLockoutError
DB_REP_LOCKOUT
DBRepUnavailError
DB_REP_UNAVAIL
DBRunRecoveryError
DB_RUNRECOVERY
DBSecondaryBadError
DB_SECONDARY_BAD
DBVerifyBadError
DB_VERIFY_BAD
DBNoServerError
DB_NOSERVER
DBNoServerHomeError
DB_NOSERVER_HOME
DBNoServerIDError
DB_NOSERVER_ID
DBInvalidArgError
EINVAL
DBAccessError
EACCES
DBNoSpaceError
ENOSPC
DBNoMemoryError
DB_BUFFER_SMALL
DBAgainError
EAGAIN
DBBusyError
EBUSY
DBFileExistsError
EEXIST
DBNoSuchFileError
ENOENT
DBPermissionsError
EPERM
Other Package Modules¶
dbshelve.py: This is an implementation of the standard Python shelve concept for storing objects that uses bsddb3 specifically, and also exposes some of the more advanced methods and capabilities of the underlying DB.
dbtables.py: This is a module by Gregory Smith that implements a simplistic table structure on top of a DB.
dbutils.py: A catch-all for python code that is generally useful when working with DB’s
dbobj.py: Contains subclassable versions of DB and DBEnv.
dbrecio.py: Contains the DBRecIO class that can be used to do partial reads and writes from a DB record using a file-like interface. Contributed by Itamar Shtull-Trauring.
 PyUnit is used and the tests are structured such that they can be run unattended and automated. There are currently 482 test cases! (March 2010)
Reference¶
See the C language API online documentation on Oracle’s website for more details of the functionality of each of these methods. The names of all the Python methods should be the same or similar to the names in the C API.
Berkeley DB is very powerful and versatile, but it is complex to use correctly. Oracle documentation is very complete. Please, review it.
NOTE: All the methods shown below having more than one keyword argument are actually implemented using keyword argument parsing, so you can use keywords to provide optional parameters as desired. Those that have only a single optional argument are implemented without keyword parsing to help keep the implementation simple. If this is too confusing let me know and I’ll think about using keywords for everything.


an introductory paragraph with more information about yourself, including what things you're passionate about, programming languages you enjoy writing in, etc.
I enjoy writing front-end code with these technologies
you can contact me by thinking of me and I will come to you in your dream.mail box


Southern Karoo Aeromagnetic Data Acquisition and Processing
A brief update about a geophysical survey in the Karoo and what it involves
Created by Martin Bentley / @astonsplat
<EMAIL_ADDRESS>22 April 2016
Of geological interest because:
- Unbroken deposition over huge span of time (~340 to ~180Ma)
- Potentially economic resources of gas
Unfortunately....
aka: Why?
So....
aka: How do we fix this?
NMMU baseline study
Looking at:
- Groundwater
- Plants
- Water Invertebrates
- Social aspects
Geophysics
More data
New dataset at much higher resolution
Try and view in 3D
Make it easier to work with this data
Current Status
aka: What have we done so far?
- Implementing filters
- Writing - 2 and a bit chapters reviewed and being revised
What's next?
aka: What are you doing in the near future?
Permissions
- Flying survey
- Testing filters
- Begin front-end work
- Begin 3D Visualisation
What's being delivered?
- Better information for making decisions
- Software for analyses
- Various datasets:
- Aeromagnetic
- Radiometric
- Farm boundaries/Contacts
"So how's your MSc going?"
aka: Are you on track?
Short answer:
Longer answer:
- Survey took longer to get off the ground
- Implementing filters


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This password is for applications like UW email, MyUW, Web Publishing, etc.


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DES_MODES
Section: OpenSSL (7)Updated: 2007-02-27
Index Return to Main Contents
NAME
des_modes - the variants of DES and other crypto algorithms of OpenSSLDESCRIPTION
Several crypto algorithms for OpenSSL can be used in a number of modes. Those are used for using block ciphers in a way similar to stream ciphers, among other things.OVERVIEW
Electronic Codebook Mode (ECB)
Normally, this is found as the function algorithm_ecb_encrypt().- *
- 64 bits are enciphered at a time.
- *
- The order of the blocks can be rearranged without detection.
- *
- The same plaintext block always produces the same ciphertext block (for the same key) making it vulnerable to a 'dictionary attack'.
- *
- An error will only affect one ciphertext block.
Cipher Block Chaining Mode (CBC)
Normally, this is found as the function algorithm_cbc_encrypt(). Be aware that des_cbc_encrypt() is not really DES CBC (it does not update the IV); use des_ncbc_encrypt() instead.- *
- a multiple of 64 bits are enciphered at a time.
- *
- The CBC mode produces the same ciphertext whenever the same plaintext is encrypted using the same key and starting variable.
- *
- The chaining operation makes the ciphertext blocks dependent on the current and all preceding plaintext blocks and therefore blocks can not be rearranged.
- *
- The use of different starting variables prevents the same plaintext enciphering to the same ciphertext.
- *
- An error will affect the current and the following ciphertext blocks.
Cipher Feedback Mode (CFB)
Normally, this is found as the function algorithm_cfb_encrypt().- *
- a number of bits (j) <= 64 are enciphered at a time.
- *
- The CFB mode produces the same ciphertext whenever the same plaintext is encrypted using the same key and starting variable.
- *
- The chaining operation makes the ciphertext variables dependent on the current and all preceding variables and therefore j-bit variables are chained together and can not be rearranged.
- *
- The use of different starting variables prevents the same plaintext enciphering to the same ciphertext.
- *
- The strength of the CFB mode depends on the size of k (maximal if j == k). In my implementation this is always the case.
- *
- Selection of a small value for j will require more cycles through the encipherment algorithm per unit of plaintext and thus cause greater processing overheads.
- *
- Only multiples of j bits can be enciphered.
- *
- An error will affect the current and the following ciphertext variables.
Output Feedback Mode (OFB)
Normally, this is found as the function algorithm_ofb_encrypt().- *
- a number of bits (j) <= 64 are enciphered at a time.
- *
- The OFB mode produces the same ciphertext whenever the same plaintext enciphered using the same key and starting variable. More over, in the OFB mode the same key stream is produced when the same key and start variable are used. Consequently, for security reasons a specific start variable should be used only once for a given key.
- *
- The absence of chaining makes the OFB more vulnerable to specific attacks.
- *
- The use of different start variables values prevents the same plaintext enciphering to the same ciphertext, by producing different key streams.
- *
- Selection of a small value for j will require more cycles through the encipherment algorithm per unit of plaintext and thus cause greater processing overheads.
- *
- Only multiples of j bits can be enciphered.
- *
- OFB mode of operation does not extend ciphertext errors in the resultant plaintext output. Every bit error in the ciphertext causes only one bit to be in error in the deciphered plaintext.
- *
- OFB mode is not self-synchronizing. If the two operation of encipherment and decipherment get out of synchronism, the system needs to be re-initialized.
- *
- Each re-initialization should use a value of the start variable different from the start variable values used before with the same key. The reason for this is that an identical bit stream would be produced each time from the same parameters. This would be susceptible to a 'known plaintext' attack.
Triple ECB Mode
Normally, this is found as the function algorithm_ecb3_encrypt().- *
- Encrypt with key1, decrypt with key2 and encrypt with key3 again.
- *
- As for ECB encryption but increases the key length to 168 bits. There are theoretic attacks that can be used that make the effective key length 112 bits, but this attack also requires 2^56 blocks of memory, not very likely, even for the NSA.
- *
- If both keys are the same it is equivalent to encrypting once with just one key.
- *
- If the first and last key are the same, the key length is 112 bits. There are attacks that could reduce the effective key strength to only slightly more than 56 bits, but these require a lot of memory.
- *
- If all 3 keys are the same, this is effectively the same as normal ecb mode.
Triple CBC Mode
Normally, this is found as the function algorithm_ede3_cbc_encrypt().- *
- Encrypt with key1, decrypt with key2 and then encrypt with key3.
- *
- As for CBC encryption but increases the key length to 168 bits with the same restrictions as for triple ecb mode.
NOTES
This text was been written in large parts by Eric Young in his original documentation for SSLeay, the predecessor of OpenSSL. In turn, he attributed it to:
AS 2805.5.2 Australian Standard Electronic funds transfer - Requirements for interfaces, Part 5.2: Modes of operation for an n-bit block cipher algorithm Appendix A
SEE ALSO
blowfish(3), des(3), idea(3), rc2(3)
Index
This document was created by man2html, using the manual pages.
Time: 05:34:14 GMT, December 24, 2015


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Democratization of data requires not only open access, but publication in standards that other researchers can take advantage of. Currently, the only data modelling framework that represents an international standard is the CIDOC-CRM (ISO 21127:2006, and the updated ISO 21127:2014).
Documentation is essential to all aspects of a museums activities. Collections without adequate documentation are not true "museum" collections.
CIDOC, ICOM's International Committee for Documentation, provides the museum community with advice on good practice and developments in museum documentation.
CIDOC Conceptual Reference Model (CRM)
The CIDOC Conceptual Reference Model (CRM) provides definitions and a formal structure for describing the implicit and explicit concepts and relationships used in cultural heritage documentation.
The CIDOC CRM is intended to promote a shared understanding of cultural heritage information by providing a common and extensible semantic framework that any cultural heritage information can be mapped to. It is intended to be a common language for domain experts and implementers to formulate requirements for information systems and to serve as a guide for good practice of conceptual modelling. In this way, it can provide the "semantic glue" needed to mediate between different sources of cultural heritage information, such as that published by museums, libraries and archives.


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__all__ is a Sacred Space and you Murderous Goblins are all Profaning it
Let me spell something out for you trickster-meanies:
# HELLO I AM thingy.py
__all__ = [X, Y, Z]
X = True
Y = True
Z = True
Reasonable, right?
>>> from thingy import *
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/scheirer/thingy.py", line 1, in <module>
__all__ = [X, Y, Z]
NameError: name 'X' is not defined
Python is older than my son (who is 3) and yet you abuse it. You monsters. You goblins. You haters. You fuckerinos.

Oh my god my irrational hate has fueled in me a series of topics I care to “engage” about (COMMENTS DISABLED).


Calculator
Takes key-strokes and button presses
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atomic_input_dialog/[2,3,4]
atomic_input_dialog/[2,3,4] | - input atoms and numbers
FORMS
atomic_input_dialog(Msg, Input)
atomic_input_dialog(Msg, Title, Input)
atomic_input_dialog(Interp, Msg, Title, Input)
DESCRIPTION
These predicates present the user with popup dialogs requesting input of text
(as atoms) or numbers. The shorter versions are defined by:
atomic_input_dialog(Msg, Input)
:-
atomic_input_dialog(Msg, ‘Input’, Input).
atomic_input_dialog(Msg, Title, Input)
:-
atomic_input_dialog(tcli, Msg, Title, Input).
For the general call
atomic_input_dialog(Interp, Msg, Title, Input)
Interp, Msg, and Title should all be atoms, while Input should be an uninstantiated variable.
EXAMPLES
The call
?- atomic_input_dialog(‘Please input something:’,
Input).
will popup the following window:
If the user types
Logic is wonderful
then the result would be
Atom = Logic is wonderful


and the {background margin {border-style px;} #ffffff;} for will The many from have not are been cancer px; which {font-family font-size has research with COVID- double; patients that their Cancer United border-color border-width but full had major medical response solid; staff this {font-style {font-weight Journal Not Research This already applicable bold courier; em; general may particularly population pt;} these was work


This pug file is created to test pug-lint tools, such as "SublimeLinter-pug-lint". Suppose you should see 2 things:
Link
text


Backpropagation in Neural Network (NN) with Python
Explaining backpropagation on the three layer NN in Python using numpy library.
Content
Theory and experimental results (on this page):
- Three Layers NN
- Mathematical calculations
- Backpropagation
- Writing a code in Python
- Results
- Analysis of results
Three Layers NN
In order to solve more complex tasks, apart from that was described in the Introduction part, it is needed to use more layers in the NN. In this case the weights will be updated sequentially from the last layer to the input layer with respect to the confidance of the current results. This approach is called Backpropagation.
Consider three layers NN.
On the figure below the NN is shown.
It has Input layer (Layer 0), Hidden Layer (Layer 1), Output Layer (Layer 2).
In the Layer 0 there are three parameters to be considered, in the Layer 1 there are four Hidden Neurons and in the Layer 2 there is one Output Neuron.
Consider the same inputs from Introduction part.
Training sets and Desired results are shown below.
- Set of inputs #1 = 1 1 1; Output #1 = 1
- Set of inputs #2 = 1 0 1; Output #2 = 1
- Set of inputs #3 = 0 0 1; Output #3 = 0
- Set of inputs #4 = 0 1 1; Output #4 = 0
Testing set is shown below.
- Set of inputs #5 = 1 0 0</b>
Weights 0-1 corresponds to the weights between Layer 0 and Layer 1.
Weights 1-2 corresponds to the weights between Layer 1 and Layer 2.
Mathematical calculations
By using matrices it is possible to calculate the output for each set of inputs.
On the figure below operations between matrices are shown.
First matrix corresponds to the inputs (Layer 0) and is multiplied by matrix of weights. As a result, the matrix with values for hidden layer (Layer 1) received which is further multiplied by another matrix of weights. And matrix with outputs (Layer 2) finally received.
Backpropagation
Updating the weights is the process of adjusting or training the NN in order to get more accurate result. Backpropagation updates weights from last layer to the first layer.
- Firstly, the error for the output Layer 2 is calculated, which is the difference between desired output and received output, and this is the error for the last output layer (Layer 2):
layer_2_error = Desired data - Received data - Secondly, the delta for the output Layer 2 is calculated, which is used for correction the Weights 1-2 and for finding the error for the hidden layer (Layer 1). The adjustments will be done in proportion to the value of error by using Sigmoid Gradient that was described in the Introduction part and with respect to Gradient descent:
delta_2 = layer_2_error * layer_2 * (1 - layer_2) - Thirdly, the error for the hidden Layer 1 is calculated, multiplying delta_2 by Weights 1-2. In this way the comparison of influence of the hidden layer (Layer 1) to the output error is evaluated:
layer_1_error = delta_2 * weights_1_2 - Finally, the delta for the hidden Layer 1 is calculated for correction the Weights 1-2:
delta_1 = layer_1_error * layer_1 * (1 - layer_1)
Just steps are shown below:
- layer_2_error = Desired data - Received data
- delta_2 = layer_2_error * layer_2 * (1 - layer_2)
- layer_1_error = delta_2 * weights_1_2
- delta_1 = layer_1_error * layer_1 * (1 - layer_1)
After the deltas for last (Layer 2) and hidden (Layer 1) layers were found, the weights are updated by multiplying matrices of outputs from layers on appropriate delta:
- weights_1_2 += Layer_1 * delta_2
- weights_0_1 += Layer_0 * delta_1
On the figure below appropriate matrices are shown.
Writing a code in Python
To write a code in Python for building and training three layers NN we will use numpy library to deal with matrices.
# Creating a three layers NN by using mathematical 'numpy' library
# Using methods from the library to operate with matrices
# Importing 'numpy' library
import numpy as np
# Importing 'matplotlib' library to plot experimental results in form of figures
import matplotlib.pyplot as plt
# Creating a class for three layers Neural Network
class ThreeLayersNeuralNetwork():
def __init__(self):
# Using 'seed' for the random generator
# It'll return the same random numbers each time the program runs
# Very useful for debugging
np.random.seed(1)
# Modeling three layers Neural Network
# Input layer (Layer 0) has three parameters
# Hidden layer (Layer 1) has four neurons
# Output layer (Layer 2) has one neuron
# Initializing weights between input and hidden layers
# The values of the weights are in range from -1 to 1
# We receive matrix 3x4 of weights (3 inputs in Layer 0 and 4 neurons in Layer 1)
self.weights_0_1 = 2 * np.random.random((3, 4)) - 1
# Initializing weights between hidden and output layers
# The values of the weights are in range from -1 to 1
# We receive matrix 4x1 of weights (4 neurons in Layer 1 and 1 neuron in Layer 2)
self.weights_1_2 = 2 * np.random.random((4, 1)) - 1
# Creating a variable for storing matrix with output results
self.layer_2 = np.array([])
# Creating function for normalizing weights and other results by Sigmoid curve
def normalizing_results(self, x):
return 1 / (1 + np.exp(-x))
# Creating function for calculating a derivative of Sigmoid function (gradient of Sigmoid curve)
# Which is going to be used for back propagation - correction of the weights
# This derivative shows how good is the current weights
def derivative_of_sigmoid(self, x):
return x * (1 - x)
# Creating function for running and testing NN after training
def run_nn(self, set_of_inputs):
# Feed forward through three layers in NN
# Results are returned in normalized form in appropriate dimensions
layer_0 = set_of_inputs # matrix 1x3
layer_1 = self.normalizing_results(np.dot(layer_0, self.weights_0_1)) # matrix 1x3 * matrix 3x4 = matrix 1x4
layer_2 = self.normalizing_results(np.dot(layer_1, self.weights_1_2)) # matrix 1x4 * matrix 4x1 = matrix 1x1
return layer_2
# Creating function for training the NN
def training_process(self, set_of_inputs_for_training, set_of_outputs_for_training, iterations):
# Training NN desired number of times
for i in range(iterations):
# Feeding our NN with training set and calculating output
# Feed forward through three layers in NN
# With 'numpy' library and function 'dot' we multiply matrices with values in layers to appropriate weights
# Results are returned in normalized form in appropriate dimensions
layer_0 = set_of_inputs_for_training # matrix 4x3
layer_1 = self.normalizing_results(np.dot(layer_0, self.weights_0_1)) # matrix 4x3 * matrix 3x4 = matrix 4x4
self.layer_2 = self.normalizing_results(np.dot(layer_1, self.weights_1_2)) # matrix 4x4 * matrix 4x1 = matrix 4x1
# Using Backpropagation for calculating values to correct weights
# Calculating an error for output layer (Layer 2) which is the difference between desired output and obtained output
# We subtract matrix 4x1 of received outputs from matrix 4x1 of desired outputs
layer_2_error = set_of_outputs_for_training - self.layer_2
# Showing the error each 500 iterations to track the improvements
# Comment before analysis to prevent extra information to be shown
if (i % 500) == 0:
print('Final error after', i, 'iterations =', np.mean(np.abs(layer_2_error)))
# Final error after 0 iterations = 0.4685343254580603
# Final error after 500 iterations = 0.027359665117498422
# Final error after 1000 iterations = 0.018014239352682853
# Final error after 1500 iterations = 0.01424538015492187
# Final error after 2000 iterations = 0.01209811882788332
# Final error after 2500 iterations = 0.01067390131321088
# Final error after 3000 iterations = 0.009643630560125674
# Final error after 3500 iterations = 0.008855140776037976
# Final error after 4000 iterations = 0.008227317734746435
# Final error after 4500 iterations = 0.007712523196874705
# Calculating delta for output layer (Layer 2)
# Using sign '*' instead of function 'dot' of numpy library
# In this way matrix 4x1 will be multiplied by matrix 4x1 element by element
# For example,
# n = np.array([[1], [1], [1], [1]])
# m = np.array([[2], [3], [4], [5]])
# n * m = [[2], [3], [4], [5]]
# That is what we need now for this case
delta_2 = layer_2_error * self.derivative_of_sigmoid(self.layer_2)
# Calculating an error for hidden layer (Layer 1)
# Multiplying delta_2 by weights between hidden layer and output layer
# Shows us how much hidden layer (Layer 1) influences on to the layer_2_error
layer_1_error = np.dot(delta_2, self.weights_1_2.T)
# Calculating delta for hidden layer (Layer 1)
delta_1 = layer_1_error * self.derivative_of_sigmoid(layer_1)
# Implementing corrections of weights
self.weights_1_2 += np.dot(layer_1.T, delta_2)
self.weights_0_1 += np.dot(layer_0.T, delta_1)
# Creating three layers NN by initializing of instance of the class
three_layers_neural_network = ThreeLayersNeuralNetwork()
# Showing the weights of synapses initialized from the very beginning randomly
# Weights 0-1 between input layer (Layer 0) and hidden layer (Layer 1)
print('Weights 0-1')
print(three_layers_neural_network.weights_0_1)
print()
# [[-0.16595599 0.44064899 -0.99977125 -0.39533485]
# [-0.70648822 -0.81532281 -0.62747958 -0.30887855]
# [-0.20646505 0.07763347 -0.16161097 0.370439 ]]
# Weights 1-2 between hidden layer (Layer 1) and output layer (Layer 2)
print('Weights 1-2')
print(three_layers_neural_network.weights_1_2)
print()
# [[-0.5910955 ]
# [ 0.75623487]
# [-0.94522481]
# [ 0.34093502]]
# Creating a set of inputs and outputs for the training process
# Using function 'array' of the 'numpy' library
# Creating matrix 4x3
input_set_for_training = np.array([[1, 1, 1], [1, 0, 1], [0, 0, 1], [0, 1, 1]])
# Create matrix 1x4 and transpose it to matrix 4x1 at the same time
output_set_for_training = np.array([[1, 1, 0, 0]]).T
# Starting the training process with data above and number of repetitions of 5000
three_layers_neural_network.training_process(input_set_for_training, output_set_for_training, 5000)
# Showing the output results after training
print()
print('Output results after training:')
print(three_layers_neural_network.layer_2)
print()
# [[0.99181271]
# [0.9926379 ]
# [0.00744159]
# [0.00613513]]
# After training process was finished and weights are adjusted we can test NN
# The data for testing is [1, 0, 0]
# The expected output is 1
print('Output result for testing data = ', three_layers_neural_network.run_nn(np.array([1, 0, 0])))
# Congratulations! The output is equal to 0.99619533 which is very close to 1
# [0.99619533]
Results
Set of inputs and outputs for the training process:
input_set_for_training = np.array([[1, 1, 1], [1, 0, 1], [0, 0, 1], [0, 1, 1]])
The output set we transposes into the vector with function ‘T’:
output_set_for_training = np.array([[1, 1, 0, 0]]).T
Weights of synapses initialized from the very beginning randomly
[[-0.16595599 0.44064899 -0.99977125 -0.39533485]
[-0.70648822 -0.81532281 -0.62747958 -0.30887855]
[-0.20646505 0.07763347 -0.16161097 0.370439 ]]
Weights 1-2 between hidden layer (Layer 1) and output layer (Layer 2)
[[-0.5910955 ]
[ 0.75623487]
[-0.94522481]
[ 0.34093502]]
The data for testing after training is [1, 0, 0] and the expected output is 1.
Result is:
[0.99619533]
Congratulations! The output is equal to 0.99619533 which is very close to 1.
Analysis of results
Analysing obtained results and building the figure with Outputs and number of Iterations in order to understand the raising accuracy of output and needed amount of iterations for training.
Consider following part of the code:
# Analysis of results
# It is better to comment two lines in the class 'ThreeLayersNeuralNetwork' before analysis:
'''
if (i % 500) == 0:
print('Final error after', i, 'iterations =', np.mean(np.abs(layer_2_error)))
'''
# In order not to show extra information
# Finding needed amount of iterations to reach curtain accuracy
# Creating list to store the resulting data
lst_result = []
# Creating list to store the number of iterations for each experiment
lst_iterations = []
# Creating a loop and collecting resulted output data with different numbers of iterations
# From 10 to 1000 with step=10
for i in range(10, 1000, 10):
# Creating new instance of the NN class each time
# In order not to be influenced from the previous training results
three_layers_neural_network_analysis = ThreeLayersNeuralNetwork()
# Starting the training process with number of repetitions equals to i
three_layers_neural_network_analysis.training_process(input_set_for_training, output_set_for_training, i)
# Collecting number of iterations in the list
lst_iterations += [i]
# Now we run trained NN with data for testing and obtain the result
output = three_layers_neural_network_analysis.run_nn(np.array([1, 0, 0]))
# Collecting resulted outputs in the list
lst_result += [output]
# Plotting the results
plt.figure()
plt.plot(lst_iterations, lst_result, 'b')
plt.title('Iterations via Output')
plt.xlabel('Iterations')
plt.ylabel('Output')
# Showing the plot
plt.show()
As a result we get our figure:
We can see that after 300 iterations the accuracy doesn’t change too much.
But how to calculate the exact number of iterations to achieve needed accuracy?
Consider final part of the code:
# Find exact number of iterations in order to reach specific accuracy
i = 10 # Iterations
output = 0.1 # Output
# Creating a while loop and training NN
# Stop when output reached accuracy 0.99
while output < 0.99:
# Creating new instance of the NN class each time
# In order not to be influenced from the previous training results above
three_layers_neural_network_analysis_1 = ThreeLayersNeuralNetwork()
# Starting the training process with number of repetitions equals to i
three_layers_neural_network_analysis_1.training_process(input_set_for_training, output_set_for_training, i)
# Now we run trained NN with data for testing and obtain the result
output = three_layers_neural_network_analysis_1.run_nn(np.array([1, 0, 0]))
# Increasing the number of iterations
i += 10
# Showing the found number of iterations for accuracy 0.99
print()
print('Number of iterations to reach accuracy 0.99 is', i) # Needed numbers of iterations is equal to 1010
As we can see, to reach the accuracy 0.99 we need 1010 iterations.
Full code is available here: Intro_simple_three_layers_NN.py
MIT License
Copyright (c) 2018 Valentyn N Sichkar
github.com/sichkar-valentyn
Reference to:
Valentyn N Sichkar. Neural Networks for computer vision in autonomous vehicles and robotics // GitHub platform. DOI: 10.5281/zenodo.1317904


This is a test file to insure that everything is working well.
require 'blahblah' #this is a comment def something is something end


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Bullet Collision Detection & Physics Library
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#include <btList.h>
Public Member Functions | |
| btGEN_List () | |
| btGEN_Link * | getHead () const |
| btGEN_Link * | getTail () const |
| void | addHead (btGEN_Link *link) |
| void | addTail (btGEN_Link *link) |
Private Attributes | |
| btGEN_Link | m_head |
| btGEN_Link | m_tail |
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inline |
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inline |
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inline |
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inline |
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private |
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private |


This application will use some of your credentials only fore registration purpose.
No further actions will be taken and using your credentials for spam.
It is just for registration and allowing you to use HTML2PDF


Recently I had the chance to participate in the BSides Ottawa CTF. While I had a great time, I noticed a few issues; mainly the fact that the facebook CTF platform doesn't offer the ability to keep track of which flags have already been found. While the system does show the compromised countries, it does not show which flag was actually captured. This led to a few issues where people ended up wasting time because they were both finding the same flag, as they did not know each other's progress.
CTFort aims to act as a centralized progress tracker towards flag captures. All you need to do to get started is log in/register and then create a team. Once a team has been created, you can invite your friends using the provided invitation code. They just need to enter this code when joining the team and they will be in. Once a part of the team, each of the members will have access to the various modules to enhance their CTF experience.


Attempts to rate the suitability of land for food production probably began with the dawn of arable agriculture (Simonson, 1968). From the early days of soil survey in Canada, ratings were made of the agricultural potentials of mapped areas (McKeague and Stobbe, 1978). The first national land inventory in Canada, the Canada Land Inventory (CLI) (ARDA, 1965), was based largely on soil survey information, and the initial capability rating was for common field crops. Over the years several agencies modified the CLI for a variety of purposes. In some cases, new systems of land capability rating were developed. The use of different systems led to confusion and conflict. Concern about this was expressed at the meeting of the Expert Committee on Soil Survey in 1986, and the Land Resource Research Centre responded in 1987 with the formation of an Agronomic Interpretations Working Group, with representation from all regions of Canada.
The Working Group examined a number of systems used to rate land for the production of agricultural crops. They included the systems used in British Columbia (Kenk and Cotic, 1983), Alberta (Alberta Soils Advisory Committee, 1987) and the Ottawa area (Marshall et al., 1979) as well as Ontario (Brokx and Presant, 1986), Quebec (Mailloux et al., 1964) and the Atlantic Region (Atlantic Advisory Committee, 1988). Several climatic stratifications were also reviewed (Chapman and Brown, 1966; FAO, 1976; Williams, 1983). Conclusions from the initial assessment were:
a) the inadequate consideration of climate,
b) the omission of organic soils from the system,
c) the inadequate documentation of criteria, and
d) the subjectivity of the rating process.
It was agreed that:
With the above direction, the Working Group proceeded with exploratory work in 1988, development of a climatic framework for the system in 1989, preparation of a draft report in 1990, and testing and modification of the system in 1991. The result was a "working document published in 1992 which was circulated to Land Resource Units across the country for exposure to local clients and further testing. Feedback from this phase was used for further modification and clarification to produce the present document. At the same time a computer program, written in dBASE, was developed for automated calculations.
This document should be viewed both as a general procedure for assessing land suitability for crop production and as a specific system for rating land suitability for spring-seeded small grains. The spring-seeded small grains, which include wheat, barley and oats, were selected to develop the procedure and format because they can be grown throughout the agriculture area of Canada. The system should work equally well for hardy oilseeds such as canola and flax which have similar land resource requirements. As the CLI ratings were designed for common field crops suited to the area (ARDA, 1965), the two systems should be roughly similar. The system described herein includes most of the attributes of an optimum approach to soil productivity rating as outlined by Huddleston (1984).
The principal objectives of the working group were to clarify and specify the input parameters so that the rating could be documented and automated, to develop a uniform national approach and to provide for expansion of the rating to a variety of crops. As such, this report does not present new information so much as to reorganize, document and specify our present approaches and knowledge. In so doing, it also identifies areas of weakness and directs testing and research for improvements. It is an attempt to improve the quality and particularly the uniformity of land suitability rating in Canada.