Functions | |
| GLM_FUNC_DECL uint | glm::packUnorm2x16 (vec2 const &v) |
| GLM_FUNC_DECL uint | glm::packSnorm2x16 (vec2 const &v) |
| GLM_FUNC_DECL uint | glm::packUnorm4x8 (vec4 const &v) |
| GLM_FUNC_DECL uint | glm::packSnorm4x8 (vec4 const &v) |
| GLM_FUNC_DECL vec2 | glm::unpackUnorm2x16 (uint const &p) |
| GLM_FUNC_DECL vec2 | glm::unpackSnorm2x16 (uint const &p) |
| GLM_FUNC_DECL vec4 | glm::unpackUnorm4x8 (uint const &p) |
| GLM_FUNC_DECL vec4 | glm::unpackSnorm4x8 (uint const &p) |
| GLM_FUNC_DECL double | glm::packDouble2x32 (uvec2 const &v) |
| GLM_FUNC_DECL uvec2 | glm::unpackDouble2x32 (double const &v) |
| GLM_FUNC_DECL uint | glm::packHalf2x16 (vec2 const &v) |
| GLM_FUNC_DECL vec2 | glm::unpackHalf2x16 (uint const &v) |
| GLM_FUNC_QUALIFIER double glm::packDouble2x32 | ( | uvec2 const & | v | ) |
Returns a double-precision value obtained by packing the components of v into a 64-bit value. If an IEEE 754 Inf or NaN is created, it will not signal, and the resulting floating point value is unspecified. Otherwise, the bit- level representation of v is preserved. The first vector component specifies the 32 least significant bits; the second component specifies the 32 most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 91 of file func_packing.inl.
| GLM_FUNC_QUALIFIER uint glm::packHalf2x16 | ( | vec2 const & | v | ) |
Returns an unsigned integer obtained by converting the components of a two-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these two 16- bit integers into a 32-bit unsigned integer. The first vector component specifies the 16 least-significant bits of the result; the second component specifies the 16 most-significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 101 of file func_packing.inl.
00102 { 00103 i16vec2 Unpack( 00104 detail::toFloat16(v.x), 00105 detail::toFloat16(v.y)); 00106 00107 uint * Result = reinterpret_cast<uint*>(&Unpack); 00108 return *Result; 00109 }
| GLM_FUNC_QUALIFIER uint glm::packSnorm2x16 | ( | vec2 const & | v | ) |
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. Then, the results are packed into the returned 32-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packSnorm2x16: round(clamp(v, -1, +1) * 32767.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 49 of file func_packing.inl.
References glm::clamp(), and glm::round().
00050 { 00051 i16vec2 Topack(round(clamp(v ,-1.0f, 1.0f) * 32767.0f)); 00052 // return reinterpret_cast<uint32&>(Topack); 00053 uint* ptr(reinterpret_cast<uint*>(&Topack)); 00054 return *ptr; 00055 }
| GLM_FUNC_QUALIFIER uint glm::packSnorm4x8 | ( | vec4 const & | v | ) |
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. Then, the results are packed into the returned 32-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packSnorm4x8: round(clamp(c, -1, +1) * 127.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 77 of file func_packing.inl.
References glm::clamp(), and glm::round().
00078 { 00079 i8vec4 Topack(round(clamp(v ,-1.0f, 1.0f) * 127.0f)); 00080 return reinterpret_cast<uint&>(Topack); 00081 }
| GLM_FUNC_QUALIFIER uint glm::packUnorm2x16 | ( | vec2 const & | v | ) |
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. Then, the results are packed into the returned 32-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packUnorm2x16: round(clamp(c, 0, +1) * 65535.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 35 of file func_packing.inl.
References glm::clamp(), and glm::round().
00036 { 00037 u16vec2 Topack(round(clamp(v, 0.0f, 1.0f) * 65535.0f)); 00038 // return reinterpret_cast<uint&>(Topack); 00039 uint* ptr(reinterpret_cast<uint*>(&Topack)); 00040 return *ptr; 00041 }
| GLM_FUNC_QUALIFIER uint glm::packUnorm4x8 | ( | vec4 const & | v | ) |
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. Then, the results are packed into the returned 32-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packUnorm4x8: round(clamp(c, 0, +1) * 255.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 65 of file func_packing.inl.
References glm::clamp(), and glm::round().
00066 { 00067 u8vec4 Topack(round(clamp(v, 0.0f, 1.0f) * 255.0f)); 00068 return reinterpret_cast<uint&>(Topack); 00069 }
| GLM_FUNC_QUALIFIER uvec2 glm::unpackDouble2x32 | ( | double const & | v | ) |
Returns a two-component unsigned integer vector representation of v. The bit-level representation of v is preserved. The first component of the vector contains the 32 least significant bits of the double; the second component consists the 32 most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 96 of file func_packing.inl.
00097 { 00098 return reinterpret_cast<uvec2 const &>(v); 00099 }
| GLM_FUNC_QUALIFIER vec2 glm::unpackHalf2x16 | ( | uint const & | v | ) |
Returns a two-component floating-point vector with components obtained by unpacking a 32-bit unsigned integer into a pair of 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values. The first component of the vector is obtained from the 16 least-significant bits of v; the second component is obtained from the 16 most-significant bits of v.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 111 of file func_packing.inl.
00112 { 00113 i16vec2 Unpack(reinterpret_cast<i16vec2 const &>(v)); 00114 00115 return vec2( 00116 detail::toFloat32(Unpack.x), 00117 detail::toFloat32(Unpack.y)); 00118 }
| GLM_FUNC_QUALIFIER vec2 glm::unpackSnorm2x16 | ( | uint const & | p | ) |
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm2x16: clamp(f / 32767.0, -1, +1)
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 57 of file func_packing.inl.
References glm::clamp(), and glm::e().
00058 { 00059 vec2 Unpack(reinterpret_cast<i16vec2 const &>(p)); 00060 return clamp( 00061 Unpack * 3.0518509475997192297128208258309e-5f, //1.0f / 32767.0f, 00062 -1.0f, 1.0f); 00063 }
| GLM_FUNC_QUALIFIER glm::vec4 glm::unpackSnorm4x8 | ( | uint const & | p | ) |
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm4x8: clamp(f / 127.0, -1, +1)
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 83 of file func_packing.inl.
References glm::clamp().
00084 { 00085 vec4 Unpack(reinterpret_cast<i8vec4 const &>(p)); 00086 return clamp( 00087 Unpack * 0.0078740157480315f, // 1.0f / 127.0f 00088 -1.0f, 1.0f); 00089 }
| GLM_FUNC_QUALIFIER vec2 glm::unpackUnorm2x16 | ( | uint const & | p | ) |
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm2x16: f / 65535.0
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 43 of file func_packing.inl.
References glm::e().
00044 { 00045 vec2 Unpack(reinterpret_cast<u16vec2 const &>(p)); 00046 return Unpack * float(1.5259021896696421759365224689097e-5); // 1.0 / 65535.0 00047 }
| GLM_FUNC_QUALIFIER vec4 glm::unpackUnorm4x8 | ( | uint const & | p | ) |
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm4x8: f / 255.0
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
Definition at line 71 of file func_packing.inl.
00072 { 00073 vec4 Unpack(reinterpret_cast<u8vec4 const&>(p)); 00074 return Unpack * float(0.0039215686274509803921568627451); // 1 / 255 00075 }


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class: center, middle, inverse, title-slide # Breast Cancer Classification ## ⚔
by using SVM - A Review of Asymptotic Properties ###
Yuan Du
 https://www.linkedin.com/in/yuaneldaif/
###
11-05-2019
 https://yuan-du.com
--- class: bg-main1 inverse black background-image: url("BreastCancer.jpg") background-size: contain #Model: SVM ##.green[Article : "Asymptotic normality of support vector machine variants and other regularized kernel methods" --Robert Hable ] -- ##.blue[SVM Estimator] `$$S_n: (\mathcal{X} \, X \, \mathcal{Y})^n \to H, \quad D_n \mapsto f_{L,D_n,\lambda_{D_n}}$$` ##where `\(f_{L,D_n,\lambda_{D_n}}\)` is that function `\(f \in H\)` which minimizes `$$\frac{1}{n} \sum L(x_i,y_i,f(x_i)) + \lambda_{D_n}||f||_H^2$$`. --- class: bg-main1 ##Investiagte the Asymptotic properties on estimators: -- ###.yellow[Assumptions] to prove that Empirical SVM `\(f_{L,D_n,\lambda_{D_n}}\)` is converged to theoretical SVM `\(f_{L,P,\lambda_0}\)`. This is `$$\sqrt{n}(f_{L,D_n,\lambda_{D_n}} - f_{L,P,\lambda_0})$$` converges weakly to a (zero-mean) Gaussian process in the function space H.: -- ###.yellow[1)] `\(\mathcal{X}\)` bounded and closed, `\(\mathcal{Y} \in \{-1,1\}\)` in Function space H, `\(f:\mathcal{X} \to \mathbb{R}\)`, `\((X_1,Y_1),\dots,(X_n,Y_n)\)` are independent and identically distributed according to some unknown probability measure P on `\((\mathcal{X} X \mathcal{Y},\mathcal{B}(\mathcal{X} X \mathcal{Y}))\)`. -- ###.yellow[2)] Assume Loss function is two times continuously differentiable in the third argument. This assumption is not based on any unknown entity as such model distribution P. -- ###.yellow[3)] Assume L is a P-integrable Nemitski loss function. -- ###.yellow[4)] `\(\lambda\)` is random sequence, so it's possible to use any data-driven method for choosing the regulization parameter, such as cross-validation. It's desirable to have a bound `$$0 \leq \mathcal{R}_{L,P}(f_{L,P,\lambda_0}) - \mathcal{R}_{L,P}^* \leq C(\lambda_0) \to 0 \text{for} \lambda \to 0$$` to find `\(\lambda\)` --- class: bg-main1 ##The Proof is done by below steps:  --- class: bg-main1 #.yellow[Theorem 3.1:]  --- class: bg-main1 ###.yellow[Step]1⃣ Show that `\(\sqrt{n}(\mathbb{P}_{D_n} - P)\)` converges weakly to a Gaussian process -- ###.yellow[Step]2⃣The map `\(S: P \mapsto f_{L,P,\lambda}\)` is suitably [Hadamard-differentiable](https://en.wikipedia.org/wiki/Hadamard_derivative): -- ###🅰 S is Gateaux-differentiable(first and second order differentiable). ###🅱 The Gateaux-derivative fulfills a continuity property. ###🆎 Above imply Hadamard-differentiability. -- ###.yellow[Step]3⃣: Applying a functional delta-method, show asymptotic normality of `\(\lambda\)`, `$$\sqrt{n}(\lambda_{D_n} - \lambda_0) \xrightarrow{P} 0,$$` then the risk: `$$\sqrt{n}(\mathcal{R}_{L,P}(f_{L,D_n,\lambda_{D_n}}) - \mathcal{R}_{L,P}(f_{L,P,\lambda_0})) \to \sigma \mathcal{N}(0,1)$$` --- class: bg-main1 #.yellow[Step]1⃣: CLT  --- class: bg-main1 #.yellow[Step]2⃣: Define `\(B_s\)` for Hadamard-differentiability  --- class: bg-main1 #.yellow[Step]3⃣: Prove `\(\lambda\)` is asymptotically normal so delta method can be applied  --- class: bg-main1 #.yellow[Prove Theorem 3.1:]  --- class: bg-main1 #.yellow[Final : Prove Theorem 3.2 based on Theorem 3.1]  --- class: bg-main1 #.yellow[Conclusion]: ##Asymptotic properties of support vector machines are investigated.t;br> -- ###It is shown that the difference between the empirical and the theoretical SVM is asymptotically normal with rate `\(\sqrt{n}\)`; that is, `\(\sqrt{n}(f_{L,D_n,\lambda_{D_n}} - f_{L,P,\lambda_0})\)` converges to a Gaussian process in the function space H.✔
 -- ###These results are not only of theoretical interest but also be a starting point for statistical inferences such as confidence intervals and hypothesis testing. ✔
 -- ###Without any of such assumptions,similar results are not possible as follows from the no-free-lunch theorem.✔ --- class: bg-main1 #.yellow[Application of Asymptotic properties of SVM :] -- ###-"Distributed Inference for Linear Support Vector Machine" ###Asymptotic result of parameter `\(\tilde{\beta}\)` avoids the condition on the number of machines or data batches. The estimator takes forms of weighted least square. `$$\tilde{\beta} = [\frac{1}{n} \sum_{i=1}^{n}u_i(y_i,X_i,\tilde{\beta_0})X_iX_i^T]^{-1} \{\frac{1}{n} \sum_{i=1}^{n}v_i(y_i,X_i,\tilde{\beta_0})y_iX_i - \omega(\tilde{\beta_0})\}$$` --
 ###-"Inference for Support Vector Regression under `\(\mathcal{l_1}\)` Regularization" ###Produced Asymptotically valid error bars for `\(\lambda\)` and performed `\(\mathcal{l_1}\)`-SVR rankscore test --- class: middle center bg-main1
 #😇Thank you!
 ####Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan) with [ninja themes](https://github.com/emitanaka/ninja-theme).


This provides utilities for creating classed error and warning conditions based on where the error originated.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | methods |
| Suggests: | covr, testthat |
| Enhances: | assertthat |
| Published: | 2018-12-03 |
| Author: | Andrew Redd [aut, cre], R Documentation Task Force [aut] |
| Maintainer: | Andrew Redd <Andrew.Redd at hsc.utah.edu> |
| BugReports: | https://github.com/RDocTaskForce/pkgcond/issues |
| License: | GPL-2 |
| URL: | https://github.com/RDocTaskForce/pkgcond |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README NEWS |
| CRAN checks: | pkgcond results |
| Reference manual: | pkgcond.pdf |
| Package source: | pkgcond_0.1.0.tar.gz |
| Windows binaries: | r-devel: pkgcond_0.1.0.zip, r-release: pkgcond_0.1.0.zip, r-oldrel: not available |
| OS X binaries: | r-release: pkgcond_0.1.0.tgz, r-oldrel: not available |
Please use the canonical form https://CRAN.R-project.org/package=pkgcond to link to this page.


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pycerberus is a framework to check user data thoroughly so that you can protect your application from malicious (or just garbled) input data.
pycerberus is just a Python library which uses setuptools so it does not require a special setup. It has no dependencies besides the standard Python library. There are some optional packages which you can choose to install:
pycerberus has been tested on Python 2.3-2.7 as well as Python 3. To know more about Python 3 support (and its limitations), please read the section Python 3 Support.
In every software you must check carefully that untrusted user input data matches your expectations. Unvalidated user input is a common source of security flaws. However many checks are repetitive and validation logic tends to be scattered all around the code. Because basic checks are duplicated, developers forget to check also for uncommon edge cases. Eventually there is often also some code to convert the input data (usually strings) to more convenient Python data types like int or bool.
pycerberus is a framework that tackles these common problems and allows you to write tailored validators to perform additional checks. Furthermore the framework also has built-in support for less common (but important) use cases like internationalization.
The framework itself is heavily inspired by FormEncode by Ian Bicking. Therefore most of FormEncode’s design rationale is directly applicable to pycerberus. However several things about FormEncode annoyed me so much that I decided to write my own library when I needed one for my SMTP server project pymta.
pycerberus separates validation rules (“Validators”) from the objects they validate against. It might be tempting to derive the validation rules from restrictions you specified earlier (e.g. from a class which is mapped by an ORM to a database). However that approach completely ignores that validation typically depends on context: In an API you have typically a lot more freedom in regard to allowed values compared to a public web interface where input needs to conform to a lot more checks. In a system where you declare the validation explicitly, this is possible. Also it is quite easy writing some code that generates a bottom line of validation rules automatically based on your ORM model and add additional restrictions depending on the context.
As pycerberus is completely context-agnostic (not being bundled with a specific framework), you can use it in many different places (e.g. web applications with different frameworks, server applications, check parameters in a library, …).
Further reading: FormEncode’s design rationale - most of the design ideas are also present in pycerberus.
Currently (March 2010, version 0.3) pycerberus is at a very basic stage - though with very solid foundations. The API for single validators is basically complete, i18n support is built in and there is decent documentation covering all important aspects. You can check multiple values (e.g. a web form) easily using a validation Schema (“compound validator”).
The future development will focus on repeating fields (list of values). After that, I’ll try to increase the number of built-in validators for specific domains (e.g. correct email address validation, validating host names, localized numbers). Another interesting topic will be integration into different frameworks like TurboGears and trac.
However I have to say that I’m pretty satisfied with the current status so adding more features to pycerberus won’t be my #1 priority in the next months. The current API and functionality was well-suited even when validating input parameters of a SMTP server so I think most use cases should be actually covered.
In pycerberus “Validators” are used to specify validation rules which ensure that the input matches your expectations. Every basic validator validates a just single value (e.g. one specific input field in a web application). When the validation was successful, the validated and converted value is returned. If something is wrong with the data, an exception is raised:
from pycerberus.validators import IntegerValidator
IntegerValidator().process('42') # returns 42 as int
pycerberus puts conversion and validation together in one call because of two main reasons:
Every validation error will trigger an exception, usually an InvalidDataError. This exception will contain a translated error message which can be presented to the user, a key so you can identify the exact error programmatically and the original, unmodified value:
from pycerberus.errors import InvalidDataError
from pycerberus.validators import IntegerValidator
try:
IntegerValidator().process('foo')
except InvalidDataError, e:
details = e.details()
details.msg() # u'Please enter a number.'
details.key() # 'invalid_number'
details.value() # 'foo'
details.context() # {}
You can configure the behavior of the validator when instantiating it. For example, if you pass required=False to the constructor, most validators will also accept None as a valid value:
IntegerValidator(required=True).process(None) # -> validation error
IntegerValidator(required=False).process(None) # None
Validators support different configuration options which are explained along the validator description.
All validators support an optional context argument (which defaults to an emtpy dict). It is used to plug validators into your application and make them aware of the overall system state: For example a validator must know which locale it should use to translate an error message to the correct language without relying on some global variables:
context = {'locale': 'de'}
validator = IntegerValidator()
validator.process('foo', context=context) # u'Bitte geben Sie eine Zahl ein.'
The context variable is especially useful when writing custom validators - locale is the only context information that pycerberus itself cares about.
pycerberus contains some basic validators already. You can use them as they are or use them as a basis for more specialized validators.
After all, using only built-in validators won’t help you much: You’ll need custom validation rules which means that you need to write your own validators.
pycerberus comes with two classes that can serve as a good base when you start writing a custom validator: The BaseValidator only provides the absolutely required set of API so you have maximum freedom. The Validator class itself is inherited from the BaseValidator and defines a more sophisticated API and i18n support. Usually you should use the Validator class.
The BaseValidator implements only the minimally required methods. Therefore it does not put many constraints on you. Most users probably want to use the Validator class which already implements some commonly used features.
Return the (Python) value as string which could be converted back to the given value using this validator. This is useful for widget libraries like ToscaWidgets.
Raise an InvalidDataError for the given key.
Return all keys defined by this specific validator class.
Return a message for a specific key. Implement this method if you want to avoid calls to messages() which might be costly (otherwise implementing this method is optional).
Return all messages which are defined by this validator as a key/message dictionary. Alternatively you can create a class-level dictionary which contains these keys/messages.
You must declare all your messages here so that all keys are known after this method was called.
Calling this method might be costly when you have a lot of messages and returning them is expensive. You can reduce the overhead in some situations by implementing message_for_key()
This is the method to validate your input. The validator returns a (Python) representation of the given input value.
In case of errors a InvalidDataError is thrown.
The Validator is the base class of most validators and implements some commonly used features like required values (raise exception if no value was provided) or default values in case no value is given.
This validator splits conversion and validation into two separate steps: When a value is process()``ed, the validator first calls ``convert() which performs some checks on the value and eventually returns the converted value. Only if the value was converted correctly, the validate() function can do additional checks on the converted value and possibly raise an Exception in case of errors. If you only want to do additional checks (but no conversion) in your validator, you can implement validate() and simply assume that you get the correct Python type (e.g. int).
Of course if you can also raise a ValidationError inside of convert() - often errors can only be detected during the conversion process.
By default, a validator will raise an InvalidDataError if no value was given (unless you set a default value). If required is False, the default is None. All exceptions thrown by validators must be derived from ValidationError. Exceptions caused by invalid user input should use InvalidDataError or one of the subclasses.
If strip is True (default is False) and the input value has a strip() method, the input will be stripped before it is tested for empty values and passed to the convert()/validate() methods.
In order to prevent programmer errors, an exception will be raised if you set required to True but provide a default value as well.
Convert the input value to a suitable Python instance which is returned. If the input is invalid, raise an InvalidDataError.
Return the ‘empty’ value for this validator (usually None).
Decide if the value is considered an empty value.
Perform additional checks on the value which was processed successfully before (otherwise this method is not called). Raise an InvalidDataError if the input data is invalid.
You can implement only this method in your validator if you just want to add additional restrictions without touching the actual conversion.
This method must not modify the converted_value.
pycerberus uses simple_super so you can just say ‘self.super()’ in your custom validator classes. This will call the super implementation with just the same parameters as your method was called.
Validators need to be thread-safe as one instance might be used several times. Therefore you must not add additional attributes to your validator instance after you called Validator’s constructor. To prevent unexperienced programmers falling in that trap, a ‘’Validator’’ will raise an exception if you try to set an attribute. If you don’t like this behavior, you can set ‘_is_internal_state_frozen’ to False before calling Validator’s constructor.
Now it’s time to put it all together. This validator demonstrates most of the API as explained so far:
class UnicodeValidator(Validator):
def __init__(self, max=None):
self.super()
self._max_length = max
def messages(self):
return {
'invalid_type': _(u'Validator got unexpected input (expected string, got %(classname)s).'),
'too_long': _(u'Please enter at maximum %(max_length) characters.')
}
# Alternatively you could also declare a class-level variable:
# messages = {...}
def convert(self, value, context):
try:
return unicode(value, 'UTF-8')
except Exception:
classname = value.__class__.__name__
self.error('invalid_type', value, context, classname=classname)
def validate(self, converted_value, context):
if self._max_length is None:
return
if len(converted_value) > self._max_length:
self.error('too_long', converted_value, context, max_length=self._max_length)
The validator will convert all input to unicode strings (using the UTF-8 encoding). It also checks for a maximum length of the string.
You can see that all the conversion is done in convert() while additional validation is encapsulated in validate(). This can help you keeping your methods small.
In case there is an error the error() method will raise an InvalidDataError. You select the error message to show by passing a string constant key which identifies the message. The key can be used later to adapt the user interface without relying the message itself (e.g. show an additional help box in the user interface if the user typed in the wrong password).
The error messages are declared in the messages(). You’ll notice that the message strings can also contain variable parts. You can use these variable parts to give the user some additional hints about what was wrong with the data.
Modern applications must be able to handle different languages. Internationalization (i18n) in pycerberus refers to validating locale-dependent input data (e.g. different decimal separator characters) as well as validation errors in different languages. The former aspect is not yet covered by default but you should be able to write custom validators easily.
All messages from validators included in pycerberus can be translated in different languages using the standard gettext library. The language of validation error messages will be chosen depending on the locale which is given in the state dictionary,
i18n support in pycerberus is a bit broader than just translating existing error messages. i18n becomes interesting when you write your own validators (based on the ones that come with pycerberus) and your translations need to play along with the built-in ones:
All i18n support in pycerberus aims to provide custom validators with a nice, simple-to-use API while maintaining the flexibility that serious applications need.
If you want to get translated error messages from a validator, you set the correct ‘’context’‘. formencode looks for a key named ‘locale’ in the context dictionary:
validator = IntegerValidator()
validator.process('foo', context={'locale': 'en'}) # u'Please enter a number.'
validator.process('foo', context={'locale': 'de'}) # u'Bitte geben Sie eine Zahl ein.'
Usually you don’t have to know much about how pycerberus uses gettext internally. Just for completeness: The default domain is ‘pycerberus’. By default translations (.mo files) are loaded from pycerberus.locales, with a fall back to the system-wide locale dir ‘’/usr/share/locale’‘.
To translate messages from a custom validator, you need to declare them in the messages() method and mark the message strings as translatable:
from pycerberus.api import Validator
from pycerberus.i18n import _
class MyValidator(Validator):
def messages(self):
return {
'foo': _('A message.'),
'bar': _('Another message.'),
}
# your validation logic ...
Afterwards you just have to start the usual gettext process. I always use Babel because it provides the very convenient pybabel tool which simplifies the workflow a lot:
Assume your custom validator is a subclass of a built-in validator but you don’t like the built-in translation. Of course you can replace pycerberus’ mo files directly. However there is also another way where you don’t have to change pycerberus itself:
class CustomValidatorThatOverridesTranslations(Validator):
def messages(self):
return {'empty': _('My custom message if the value is empty'),
'custom': _('A custom message')}
# ...
This validator will use a different message for the ‘empty’ error and you can define custom translations for this key in your own .po files.
The gettext framework is configurable, e.g. in which directory your .mo files are located and which domain (.mo filename) should be used. In pycerberus this is configurable by validator:
class ValidatorWithCustomGettextOptions(Validator):
def messages(self):
return {'custom': _('A custom message')}
def translation_parameters(self, context):
return {'domain': 'myapp', 'localedir': '/home/foo/locale'}
# ...
These translation parameters are passed directly to the ‘’gettext’’ call so you can read about the available options in the gettext documentation. Your parameter will be applied for all messages which were declared in your validator class (but not in others). So you can modify the parameters for your own validator but keep all the existing parameters (and translations) for built-in validators.
Sometimes you don’t want to use gettext. For instance you could store translations in a relational database so that your users can update the messages themselves without fiddling with gettext tools:
class ValidatorWithNonGettextTranslation(FrameworkValidator):
def messages(self):
return {'custom': _('A custom message')}
def translate_message(self, key, native_message, translation_parameters, context):
# fetch the translation for 'native_message' from somewhere
translated_message = get_translation_from_db(native_message)
return translated_message
You can use this mechanism to plug in arbitrary translation systems into gettext. Your translation mechanism is (again) only applied to keys which were defined by your specific validator class. If you want to use your translation system also for keys which were defined by built-in validators, you need to re-define these keys in your class as shown in the previous section.
Especially in web development you often get multiple values from a form and you want to validate all these values easily. This is where “compound validators” / “schemas” come into play. A schema contains multiple validators, one validator for every field. There’s nothing special about these validators - they are just validators like the ones I explained in the previous section. Every field validator only cares about a single value and does not see the rest of the values.
You can define a schema like this:
from pycerberus.schema import SchemaValidator
from pycerberus.validators import IntegerValidator, StringValidator
schema = SchemaValidator()
schema.add('id', IntegerValidator())
schema.add('name', StringValidator())
Afterwards the schema behaves most like all basic validators - instead of a single input value they just get a dictionary:
validated_values = schema.process({'id': '42', 'name': 'Foo Bar'})
If you declared a validator for a key which is not present in the input dict, the validator will get its ‘empty’ value instead:
id_required = SchemaValidator()
id_required.add('id', IntegerValidator(required=False))
id_required.process({}) # -> {'id': None}
id_optional = SchemaValidator()
id_optional.add('id', IntegerValidator(required=True))
id_optional.process({}) # raises an Exception because id None is not acceptable
Do not mix up the ‘default’ value with the ‘empty’ value:
IntegerValidator(default=42)
The ‘default’ value in this case is 42 but the ‘empty’ value is still None.
Please note that Schemas are ‘secure by default’ which means that the returned dictionary contains only values that were validated. If you did not add a validator for a specific key, this key won’t be included in the result.
If you need to ensure that no values with unknown keys are passed to the schema (even if those would be just dropped), you can call the method ‘’set_allow_additional_parameters(False)’‘. After that the schema will raise an exception if it finds any unknown keys.
Schemas can be an important part in your application security. Also they define some kind of interface (which parameters does your application expect). Besides the algorithmic way to build a schema there is a ‘declarative’ way so that you can review and audit your schemas easily:
class MySchema(SchemaValidator):
id = IntegerValidator()
name = StringValidator()
# using it...
schema = MySchema()
It’s absolutely the same schema but the definition is way easier to read.
All schema validators are executed even if one of the previous validators failed. Because of that you can display the user all errors at once:
schema = SchemaValidator()
schema.add('id', IntegerValidator())
schema.add('name', StringValidator())
try:
schema.process({'id': 'invalid', 'name': None})
except InvalidDataError, e:
e.error_dict() # {'id': <id validation error>, 'name': <id validation error>}
e.error_for('id') # id validation error
Sometimes you need to validate multiple fields in a schema - e.g. you need to check in a ‘change password’ action that the password is entered the same twice. Or you need to check that a certain value is higher than another value in the form. That’s where formvalidators come into play.
formvalidators are validators like all other field validators but they get the complete field dict as input, not a single item. Also formvalidators are run after all field validators successfully validated the input - therefore you have access to reasonably sane values, already converted to a handy Python data type. Opposite to simple field validators, the validation process fails immediately if one formvalidator fails.
You can add formvalidators to a form like this:
class NumbersMatch(Validator):
def validate(self, fields, context):
if fields['a'] != fields['b' ]:
self.error('no_match', fields, context=context)
schema.add_formvalidator(NumbersMatch)
Of course there is also a declarative way to use form validators:
class MySchema(SchemaValidator):
# ...
formvalidators = (NumbersMatch, )
Validation schemas are an important piece of information: On the one hand they can serve as a kind of API specification (which parameters are accepted by your application) and on the other hand they are important for security audits (which constraints are put on your input values). Obviously this is something that you want to get right - duplicating this information only increases the likelyhood of bugs.
The issue becomes especially annoying when you have a web application with a complex form (e.g. a new user registration process) that you want to split in multiple steps on different pages so that your users won’t drop out immediately when they see the huge form. It is good HTTP/ReST design practice to keep state on the client side. Therefore you pass fields from previous pages in hidden input fields to the next and for the final page it looks like there was one big form. This also has the advantage that you can shuffle the fields on the different pages without changing real logic.
With that approach your pretty much settled - however you need a separate validation schema for every single page which is a huge duplication. With pycerberus you can avoid that by using ‘’schema inheritance’‘:
class FirstPage(SchemaValidator):
id = IntegerValidator()
formvalidators = (SomeValidator(), )
class SecondPage(FirstPage):
# this schema contains also 'id' validator
name = StringValidator()
# formvalidators are implicitely appended so actually this schema has
# these formvalidators: (SomeValidator(), AnotherValidator(), )
formvalidators = (AnotherValidator(), )
class FinalPage(SecondPage):
# this schema contains also 'id' and 'name' validators
age = IntegerValidator()
# This page contains again both formvalidators
As you can see, every page adds some validators while keeping the old ones. This eliminates the duplication problem described above,
What happens if SecondPage declares a different validator for ‘id’? In this case it will just replace the ‘’IntegerValidator()’’ declared by ‘’FirstPage’‘!
I’m not using Python 3 for any project in production right now. However I’d like to support the Python 3 transition. Therefore I ensure that all tests also pass on Python 3 and I believe you can use pycerberus on Python 3 just fine. I’m testing Python 3 using the latest version of Fedora. As the Python 3 userbase is so small currently, I won’t make any effort to maintain compatibility for older versions of Python 3 (e.g. Python 3.0).
Python 3 support for pycerberus is done by the help of the wonderful ‘2to3’ tool which can do all required syntax changes for Python 3. By doing that, I can maintain a single source tree and all fixes for Python 2 are automatically in the Python 3 version.
However there are some shortcomings that you should be aware of:
IMPORTANT: If you’re using pycerberus on Python 3, please tell me how it works for you! :-)
In the common “web form” use case you already get parameters mapped to keys. That’s usually the job of your web framework. However sometimes it’s not that easy: Before you can do input validation, you need to parse the user input from a string and convert that into a dict.
This is where ‘’PositionalArgumentsParsingSchema()’’ might help you: This schema takes a string and extracts several parameters from it. So you can use it to transform "foo, 42" into dict(name="foo", value=42).
This schema parses a string containing arguments within a specified order and returns a dict where each of these parameters is mapped to a specific key for easy retrieval.
You specify the order of parameters (and the keys) in the class-level attribute parameter_order:
class ConfigListSchema(PositionalArgumentsParsingSchema):
first_key = StringValidator()
second_key = IntegerValidator()
parameter_order = (first_key, second_key)
By default the items are separated by comma though you can override in the method separator_pattern(). If there are more items than keys specified, this schema will behave like any other schema (depending if you set the class-level attribute allow_additional_parameters).
This method can manipulate or aggregate parsed arguments. In this class, it’s just a noop but sub classes can override this method to do more interesting stuff.
Return all keys defined by this specific validator class.
Return a message for a specific key. Implement this method if you want to avoid calls to messages() which might be costly (otherwise implementing this method is optional).
This schema is used for example in pymta. to parse the SMTP command strings. Also I used it in my OhlohWidgetsMacro: Trac macros can get parameters but these are passed as a single string so the schema takes care of separating these arguments.
So far I did not bother setting up a mailing list. If you have questions, please send an email to<EMAIL_ADDRESS>When there are some users for pycerberus, I’ll create a mailing list.
pycerberus is licensed under the MIT license. As there are no other dependencies (besides Python itself), you can easily use pycerberus in proprietary as well as GPL applications.


(From Wikipedia) Polypedilum nubifer or the sleeping chironomid, is a dipteran in the family Chironomidae (non-biting midges). It is not desiccation tolerant, making it an ideal species for comparison with Polypedilum vanderplanki.
Genome assembly obtained through Illumina DNA-Seq reads. Specifics can be obtained from the genome paper.
Specifics can be obtained from genome paper


This is a box with a border around it. Note which side of the box is red.
Try editing the selected example, or select a different example:
border-top-color: red;border-top-color: rgb(255, 128, 0);border-top-color: hsla(240, 50%, 25%, 0.75);border-top-color: #ffbb00;border-top-color: currentColor;border-top-color: transparent;


I am an Electrical Engineering student at Arizona State University.
Fiberli has the 4th biggest R&D center in Turkey, on lighting manufacturing area. My job was basic Circuit Design on AutoCAD and Power analysis in basic circuits.
Experience with CNC, laser cutters and SMD.
In my management training I worked every part of the company for 2 weeks. In first 2 weeks I worked in sales department and join the meeting with them in Erbil, Amsterdam, Milano, Como and Turkey. In my 3rd and 4th week I worked in Purchasing department and Marketing department. In 5th and 6th week I worked in manufacturing side but because I already worked in R&D and P&D departments I mostly focused on quality control department and IT department. In 7th and 8th weeks and 1st month of 2017 summer I was shadowing the general manager and CEO of the company and join the business meetings with them.
GPA: 3.23
GPA: 4.0
GPA: 3.86
Apart from being an engineer and etrapueneur, I enjoy most of my time being outdoors. In the winter, I am an avid Snowboarder and Hiker. I like Playing Competitive basketball and Travel diffrent countries. I have been in 19 diffrent countries till now. I really like cars and love to join car events.
Contact Me


time scale label shoule in local time when timestamp (in UTC) input, tick should align with day.
time scale label shoule in UTC time when option.useUTC is `true`, tick should not align with day.
useUTC: null, should display '00:00 01-03' in tooltip on the 1st point
useUTC: true, should display '16:00 01-02' in tooltip on the 1st point


{#-
This file was automatically generated - do not edit
NOTE: At line 9 -> 'background:' has the current color for the botom part of the cover
- current | #2a978d (OK w/ mkdocs.yaml -> palette -> /docs/assets/stylesheets)
- before | #2a978d (OK w/ mkdocs.yaml -> palette -> primary: "teal"
Blue color variations
- darker | #0A192F
- lighter | #172A45
-#}
{% extends "overrides/main.html" %}
{% block tabs %}
{{ super() }}
{% endblock %}


Node Web Site
Home
About
Concat
Weclome to Node Web Site
Lorem ipsum dolor sit amet.
Get Started
Content
Home Page


Here are some of the best mobile web development reference pages and web sites that we have come across in our mobile web development. They offer extensive, in-depth design and development information or site functionality.
Most of these are web app related as this is our focus, or applicable to both web app and device app development.


History
Computer viruses are harmful programs that appeared in the 1980’s and they had the ability to ruin the
operation of the machine. These nuisances are more specifically a type of malware that makes copies of
itself and infects the device without the user’s knowledge. While these disruptions were small at first,
they quickly became a more serious threat as computers and computer networks became more important in
people’s daily lives. In 2002, there was an estimate of 60,000 different viruses in existence.
Viruses
These days, viruses have evolved to perform numerous actions to disrupt and ruin a device.  The viruses became
more complex and it became harder for antiviruses to locate them. Today, many virus writers create a few
new lines of the harmful code each and everyday and the increase of a virus’ capabilities led to a push for
the development of antiviruses to better combat them. Nowadays, antivirus software developers keep up
with virus writers by creating protection for these the viruses they create.


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Want to hold an event at CultureHouse? Send us an email at
<EMAIL_ADDRESS>.


Samatta snubbed in 2019 CAF African Team of the Year
by Michael MwebeTHE 2019 CAF Best XI nominees list has been leaked by various reputed African media houses.
However, there is no place for Tanzania’s national team ‘Taifa Stars’ skipper Mbwana Samatta, who has scored 19 goals and grabbed six assists across all competitions for club and country so far in 2019.
Samatta could hardly have enjoyed a better 2019 year. He fired Genk to Belgium’s Jupiler League title and won the Ebony Shoe for his efforts, an award given to the best African or player of African origin in the Belgian top-flight. He also helped Tanzania qualify for the AFCON finals for the first time in 39 years.
The 26-year-old striker and 2015 best African player based in Africa has hit eight goals in 19 games this season as well as 32 goals in 51 appearances last term to attract interest from English Premier League clubs.
Uganda captain Dennis Onyango, who plays for Mamelodi Sundowns in South Africa, and Kenya’s skipper Victor Wanyama, who plies his trade in Belgium, are the only East African players on the list.
The 2019 Africa Cup of Nations (AFCON) champions Algeria dominate the list with a total of 10 players amongst the full list of 55 players.
The final team will be announced on January 7, 2020 at the CAF headquarters in Cairo at the annual awards ceremony.
Nominees in full:
Goalkeepers:
Moez Ben Cherifia, Yassine Bonnou, Mohamed El Shenawy, Sylvain, Gbohouo, Rais M'bolhi, Edouard Mendy, Richard Ofori, Andre Onana, Denis Onyango, Francis Uzoho.
Defenders:
Youcef Atal, Serge Aurier, Mehdi Benatia, Ahmed El-Mohammadi, Lamine Gassama, Faouzi Ghoulam, Achraf Hakimi, Ahmed Hegazi, Kalidou Koilibaly, Christian Luyindama, Issa Mandi, Joel Matip, Noussair Mazraoui, Yassine Meriah, Youssouf Sabaly.
Midfielders:
Andre-Frank Anguissa, Ismael Bennacer, Sofiane Feghouli, Idrissa Gueye, Tarek Hamed, Alex Iwobi, Wahbi Khazri, Naby Keita, Franck Kessie, Wilfried Ndidi, Riyad Mahrez, Thomas Partey, Mubarak Wakaso, Victor Wanyama, Hakim Ziyech.
Forwards:
Pierre Emerick Aubameyang, Jordan Ayew, Anis Badri, Cedric Bakambu, Youcef Belaili, Baghdad Bounedjah, Odion Ighalo, Sadio Mane, Moussa Marega, Mbaye Niang, Nicolas Pepe, Mohamed Salah, Islam Slimani, Percy Tau, Wilfried Zaha.


There is a new comment under your answer on question __QUESTION__.
You can view and respond to it here.
Have a great day!


Bachelors in Technology
Graduating May 2017
Information Communication Technology
CPI - 5.1
Robo-Race 1st Place in i.Fest 2013 and 2014
Line Follower 1st Place in i.Fest 2013
2nd Place i.Design 2013
Data Structures
Algorithms
Human Computer Interaction
Introduction of GPU
Data Mining
Database Management Systems


Limerick GAA confirm two players sent home following incident in New York
by James CoxLimerick GAA has confirmed that two panellists were sent home early from last weekend's New York Hurling Classic following an incident.
A statement issued on Tuesday afternoon reads: "Limerick GAA can confirm it sent home two panellists early from last weekend's New York Hurling Classic following an incident.
"Limerick GAA expects the highest standards of behaviour and example from its players, both on and off the field, and will not tolerate any breach of those standards.
"A disciplinary process has commenced and there will be no further comment in relation to this matter."


|
SDLXX
1.0.0
Modern C++17 wrapper for SDL
|
Class that represents a surface loaded from an image. More...
#include <Image.h>
Public Member Functions | |
| Image (const std::string &file) | |
| Public Member Functions inherited from sdlxx::core::Surface | |
| virtual | ~Surface () |
| Destroy the surface. More... | |
| virtual Dimensions | getSize () const final |
| Get the size of the surface. More... | |
Additional Inherited Members | |
| Static Public Member Functions inherited from sdlxx::core::Surface | |
| static Surface | fromBMP (const std::string &file) |
| Load a surface from a BMP file. More... | |
| Protected Member Functions inherited from sdlxx::core::Surface | |
| Surface (void *surface_ptr) | |
| Protected Attributes inherited from sdlxx::core::Surface | |
| void * | surface_ptr = nullptr |
Class that represents a surface loaded from an image.
| sdlxx::image::Image::Image | ( | const std::string & | file | ) |


---
layout: page
image: CLAMedia.jpg
title: "Getting Started"
subtitle: "Take your first steps."
midimage: team.jpg
midtitle:
midsubtitle:
---
Before we begin, you should have a passing understanding of what exactly “CLAMs” and the “CLAM Network” are! The CLAM software is a program that you run on your Windows, Linux or an OSX computer. The software collaborates with other people around the world to provide a secure and international payments system.
The software begins by connecting to the CLAM Network and “sync-ing” the block chain, also known as the ledger. This ledger stores and updates the history of every transaction that has ever occured on the CLAM network. The initial sync can take a bit of time - you can hover over the icon at the bottom of the application to see your progress. Additionally, you may download the “bootstrap.dat” file to speed up this process.
Once your CLAMclient software is connected and sync’d with the network, you may begin receiving and sending CLAMs with other users of the software. To send CLAMs, you simply click the send tab, input the address you would like to send to, the amount you would like to send and click send. To receive CLAMs, go to the receive tab, create an address and provide that address to the user sending you CLAM.
{% include pageMidTitle.html %}
- Download the CLAMclient.(OPTIONAL) Download the bootstrap.dat file and place it in the data directory.(OPTIONAL) Add peers to the clam.conf file in the data directory.
- Run the CLAMclient software.
- Wait for the software to sync with the network.(OPTIONAL) If you had BTC, LTC or DOGE in May of 2014, you may claim free CLAM.
- Once the CLAMclient is sync’d with the network, you can send and receive CLAMs world-wide!


[§437B-21] Civil action. Nothing in this chapter shall prohibit the bringing of a civil action against a motor vehicle repair dealer, mechanic, or apprentice by an individual. [L 1975, c 143, pt of §2]


RampRate manages billions of dollars in IT spend for some of the most important firms in the world. By leveraging RampRate’s software and services, our clients have saved millions and gained the cost transparency they need to transform and modernize their IT infrastructures.


{% extends 'auth/auth-base.html'%} {% import 'bootstrap/wtf.html' as wtf%} {% block content%}
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Abstract: Notes for Architecture and building blocks for the RandLA which is implemented in the experiment
Local feature aggregation 1.Local Spatial encoding (LocSE) –> 3+d: xyz position + other per-point features (e.g. RGB or other intermediate learned features)
Objective
**Encode xyz coordinates of all neighbouring points for each point to aware of relative spatial locations.**
Steps:
1. Finding neighbouring points
for $ith$ point, gather neighbouring points by $KNN$ for efficiency, based on Euclidean distances.
2. Relative Point Position encoding
concatenate the xyz positions of ith points, neighbour k of ith points, element-wise subtraction, Euclidean distance of two points
3. Point Feature Augmentation
Concatenate relative point position with corresponding point features f.
2.Attentive pooling Objective Aggregate the set of neighbouring point features f, use attention mechanism to automatically learn important local features. Steps: 1.Computing Attention Scores 2.Weighted Summation 3.dilated residual block


If you are developing a Wicket application, Jawr provides Components for Javascript, CSS and images. It works the same way as the JSP taglib, although there is a few extra configuration steps to take in order to use it.
There is a very small difference in the way you will use Jawr in your Wicket application, compared to using JSPs. Everything is setup the same way except that you need to reference the bundles in your pages in a different way. You need to add the jawr-wicket-extension module in your classpath. For maven users, you'll need to add the following dependency in your pom file :
<dependency> <groupId>net.jawr.extensions</groupId> <artifactId>jawr-wicket-extension</artifactId> <version>3.5</version> </dependency>
Note that this module supports the version 6.x of wicket
The first step is to declare the Jawr servlet in the web.xml descriptor. This works exactly the same as in a regular web application, so the Jawr Servlet Documentation page has all the info you need to do it. You can create the properties file normally, since the only difference in using Wicket with Jawr has to do with the reference of your web resources in your HTML page or in your Page class.
In order to use the Jawr in your Wicket application, you must initialize your application. Jawr provides an utility class, which initialize your wicket application and register the Jawr link resolver and the Jawr tag handler:
/* (non-Javadoc) * @see org.apache.wicket.protocol.http.WebApplication#init() */ protected void init() { JawrWicketApplicationInitializer.initApplication(this); }
To reference your bundles in your page, there is 2 options:
With the wicket:jawr tag, you only need to wrap your bundle references with a <wicket:jawr> tag like this:
<html xmlns:wicket="http://wicket.apache.org/dtds.data/wicket-xhtml1.4-strict.dtd" > <head> <title>Wicket Quickstart Archetype Homepage</title> <wicket:jawr> <link rel="stylesheet" type="text/css" href="/fwk/core/component.css" media="all" /> <script type="text/javascript" src="/js/bundle/msg.js" ></script> </wicket:jawr> </head> ... <wicket:jawr> <img src="/img/icons/logo.png"> <wicket:jawr> ... <wicket:jawr> <input type="image" src="/img/icons/add.png" name="add" > </wicket:jawr> ... </html>
Jawr provides 4 components for Wicket:
net.jawr.web.wicket.JawrStylesheetReference for the CSS bundles
net.jawr.web.wicket.JawrJavascriptReference for the javascript bundles
net.jawr.web.wicket.JawrHtmlImageReference for the HTML image reference
net.jawr.web.wicket.JawrImageReference for the input image reference
You can also attach a Jawr component to a web resources in your page like this:
<html xmlns:wicket="http://wicket.apache.org/dtds.data/wicket-xhtml1.4-strict.dtd" > <head> <title>Wicket Quickstart Archetype Homepage</title> <wicket:head> <link rel="stylesheet" type="text/css" wicket:id="componentCssBundle" href="/fwk/core/component.css" media="all" /> <script type="text/javascript" wicket:id="messageJsBundle" src="/js/bundle/msg.js" ></script> </wicket:head> </head> ... <img wicket:id="imgLogo" src="/img/icons/logo.png"> ... <input type="image" wicket:id="imgAdd" src="/img/icons/add.png" name="add" > ... </html>
And in your page, you must reference your component like this:
/** * Constructor that is invoked when page is invoked without a session. * * @param parameters * Page parameters */ public HomePage(final PageParameters parameters) { // Add Jawr CSS bundle component add(new JawrStylesheetReference("componentCssBundle")); // Add Jawr JS bundle component add(new JawrJavascriptReference("messageJsBundle")); // Add Jawr HTML image component add(new JawrHtmlImageReference("imgLogo")); // Add Jawr input image component add(new JawrImageReference("imgAdd")); }
That's it. As you can see in the example, the integration of Jawr is pretty simple. You only need to configure Jawr normally, initialize your application properly and then reference your bundles using the <wicket:jawr> tag in your HTML page or directly by defining the components used in your Wicket page class.


| Interface | Description |
|---|---|
| ActorHelper |
:: DeveloperApi ::
A receiver trait to be mixed in with your Actor to gain access to
the API for pushing received data into Spark Streaming for being processed.
|
| ActorReceiverData |
Case class to receive data sent by child actors
|
| BlockGeneratorListener |
Listener object for BlockGenerator events
|
| ReceivedBlock |
Trait representing a received block
|
| ReceivedBlockHandler |
Trait that represents a class that handles the storage of blocks received by receiver
|
| ReceivedBlockStoreResult |
Trait that represents the metadata related to storage of blocks
|
| ReceiverMessage |
Messages sent to the Receiver.
|
| Class | Description |
|---|---|
| ActorReceiver<T> |
Provides Actors as receivers for receiving stream.
|
| ActorSupervisorStrategy |
:: DeveloperApi ::
A helper with set of defaults for supervisor strategy
|
| ArrayBufferBlock |
class representing a block received as an ArrayBuffer
|
| BlockGenerator |
Generates batches of objects received by a
Receiver and puts them into appropriately
named blocks at regular intervals. |
| BlockManagerBasedBlockHandler |
Implementation of a
ReceivedBlockHandler which
stores the received blocks into a block manager with the specified storage level. |
| BlockManagerBasedStoreResult |
Implementation of
ReceivedBlockStoreResult
that stores the metadata related to storage of blocks using
BlockManagerBasedBlockHandler |
| ByteBufferBlock |
class representing a block received as an ByteBuffer
|
| ByteBufferData | |
| CleanupOldBlocks | |
| IteratorBlock |
class representing a block received as an Iterator
|
| IteratorData<T> | |
| RateLimiter |
Provides waitToPush() method to limit the rate at which receivers consume data.
|
| Receiver<T> |
:: DeveloperApi ::
Abstract class of a receiver that can be run on worker nodes to receive external data.
|
| ReceiverSupervisor |
Abstract class that is responsible for supervising a Receiver in the worker.
|
| ReceiverSupervisorImpl |
Concrete implementation of
ReceiverSupervisor
which provides all the necessary functionality for handling the data received by
the receiver. |
| SingleItemData<T> | |
| Statistics |
:: DeveloperApi ::
Statistics for querying the supervisor about state of workers.
|
| StopReceiver | |
| WriteAheadLogBasedBlockHandler |
Implementation of a
ReceivedBlockHandler which
stores the received blocks in both, a write ahead log and a block manager. |
| WriteAheadLogBasedStoreResult |
Implementation of
ReceivedBlockStoreResult
that stores the metadata related to storage of blocks using
WriteAheadLogBasedBlockHandler |


[gecode-users] Gecode compiling error
Christian Schulte
cschulte at kth.se
Fri May 19 16:22:09 CEST 2017
Hi Julie,
Yes, that looks like a version problem. Unfortunately, I am not using clang for development. Guido, can you comment here?
Cheers
Christian
--
Christian Schulte, www.gecode.org/~schulte
Professor of Computer Science, KTH, cschulte at kth.se
Expert Researcher, RISE SICS, christian.schulte at ri.se
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf Of Julie B
Sent: Thursday, May 18, 2017 14:22
To: users at gecode.org
Subject: [gecode-users] Gecode compiling error
Hi everyone,
I have just downloaded and installed Gecode. I am now trying to compile gecode in my main file but an undefined template error shows up.
Here is the error:
g++ -L/usr/lib -lgecodeint -lgecodesearch -lgecodekernel -lgecodesupport -lgecodedriver -lgecodeminimodel carremagique.cpp -o carremagique
In file included from carremagique.cpp:1:
In file included from /usr/local/include/gecode/driver.hh:41:
In file included from /usr/local/include/gecode/minimodel.hh:47:
In file included from /usr/local/include/gecode/kernel.hh:241:
/usr/local/include/gecode/kernel/branch-val.hpp:57:18: error: implicit instantiation of undefined template 'std::__1::function<void (Gecode::Space &, unsigned int, Gecode::IntVar, int, int)>'
BranchCommit cf;
^
/usr/local/include/gecode/int.hh:4503:31: note: in instantiation of template
class 'Gecode::ValBranch<Gecode::IntVar>' requested here
class IntValBranch : public ValBranch<IntVar> {
^
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__functional_03:206:49: note: template is declared here
template<class _Fp> class _LIBCPP_TYPE_VIS_ONLY function; // undefined
^
It comes from the include line "#include <gecode/driver.hh>" from my main file (carremagique.cpp)
I'm running under Mac OS X 10.10.5, my version of Gecode is 5.1.0 and the g++ version is:
Configured with: --prefix=/Applications/Xcode.app/Contents/Developer/usr --with-gxx-include-dir=/usr/include/c++/4.2.1
Apple LLVM version 6.0 (clang-600.0.57) (based on LLVM 3.5svn)
Target: x86_64-apple-darwin14.5.0
Thread model: posix
As no one seems to have had this error, I think I must have forgotten something, or that it's a problem of version.
I will be very thankful if you could help me.
Have a good day !
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The Story of unMonastery
unMonastery, a place-based social innovation, is aimed at addressing the interlinked needs of empty space, unemployment and depleting social services by embedding committed, skilled individuals within communities that could benefit from their presence.
unMonastery is a non-profit project that aims to challenge existing dependency chains and economic fictions, developed in collaboration with EdgeRyders LBG over the course of 18 months. Edgeryders started out as a project by the Council of Europe and the European Commission, which after termination developed into an international, community-run social enterprise.
The first unMonastery opened its doors February 2014 in Matera, Italy. Working with Matera2019, the prototype hosted projects including CoderDojo Matera, a node of the international initiative to teach children coding, Mapping the Commons, placing the city's cultural assets on OpenStreetMap, unTransit, an app to follow the city's transport system in real-time, and more. For an overview of projects, people, and the community involved in the prototype visit the unMonastery Matera website.


A fine and action-oriented comrade called me and suggested I read the
below, and then discussed my practical election plans for the future.
Here is his vision of autonomous white townships:
* * *
Plan for victory Written by Administrator
Wednesday, 05 November 2008 14:32
(source: http://www.nordwave.net/index/index....r-victory.html)
As educated and loyal National Socialists we can all agree, I’m sure,
that since [the 1967 death by assassination of George Lincoln] Rockwell
we have been failing miserably at having any kind of permanent progress
and in turn we have suffered from minimal growth and many splinters in
what was once a united movement.
Combat vet and US Navy Commander George Lincoln Rockwell. My family
in Rhode Island knew his brother. I was affiliated with his National
Socialist White People's Party in 1978-80, and wrote a number of
articles for its newspaper, White Power. (One, "Go Tell the
Spartans," provoked a letter of praise from Savitri Devi over in
Europe, who wrote Rockwell's successor, Matt Koehl, to say she wanted
to meet me -- this from White Power's editor, comrade Martin
Kerr.) Cmdr. Rockwell attended Brown University in Providence, on the
Angell Street my ancestor Thomas Angell founded. Charismatic, fearless,
tall and tough, "Rocky" (as my late friend Ralph Grandinetti, a former
Stormtrooper, called him) also had a keen sense of humor and of
showmanship, no doubt from his vaudeville father. And he knew how to
make a haranga (whence our word "harangue"), the Frankish-Germanic word for a commander's war speech,
and how to effectively ridicule the Enemy.
The lack of progress has repelled the masses putting into their mindset
that this is an idea which time will never come again. The lack of
progress has caused many splits with different leaders thinking they
can bring victory in their own ways -- which each time have failed.
We have seen almost every tactic put into effect and in turn we have seen no results. What we need is progress.
With a progressive idea we can grow exponentially as well as unite the
movement under one banner as it truly is meant to be. Since we know the
problem what is the solution?
I’m sure everyone who has been in the Movement for any length of time
is tired of talkers that never actually do anything. We all know when
building you don’t start at the roof, you build from the bottom. We
want the formation of a National Socialist state. The foundation of a
nation is the community which there are countless towns around the
nation.
Now cities have become the economic pillars that hold up the current
materialistic society we live in. These cities are over run with
non-whites and have been rotted to their very cores.
Blacks openly celebrate their separate Kwanzaa festival, and
dominate many cities, although Mexicans are crowding them out. They
demand and get their own postage stamp to celebrate separateness - and
BLACK POWER.
For our new nation we must build new communities that will become new
pillars for a new society. If we cannot at least create a small network
any potential new nation and any possible progress our race can ever
achieve will be wiped away. We have reasonable goals which we now know
must be met for the sake of our race. We must build [b]small
communities prepared for the future collapse of America which will
stand for National Socialism.
Brief Plan:
Phase 1: Creating a Blueprint.
Research the general area you personally think can serve the idea as a
good strategic area. Move to the area if you’re not already there. Then
look at small, manageable towns nearby. See what you have to work with,
finances as well as local comrades and potential newcomers to the area.
Research is a long, boring phase but without creating a blueprint for
your future communities all will fail. Each town you look at and each
person who moves to the area is progress.
Phase 2: Building a community.
This is when you take your idea and bring it into the physical realm.
This begins as soon as you buy the first piece of land in the targeted
town. By moving others to the targeted town you grow political strength
as well as economic strength in the area. Each person that moves into
the town means more "clout" affecting businesses and politicians --
since no company wants a boycott and no politician wants to be removed
from office.
Once multiple communities have been formed we can begin networking and
helping each other with whatever one community happens to lack. When
you have established a fully functional and self-dependent community of
national socialists, in which we can sustain ourselves with our own
crops and live stock as well as generate our own electricity and have
our own water supply, then we are ready to move to phase 3.
Elovena, the symbol in Finland of a wholesome white farm girl.
Phase 3: Defending the community
With every political movement and idea there must be something to
enforce it. In some states a legal militia can be formed -- and in
other states a simple gun club -- can come in use to help educate
National Socialist members of our community on survival and protection
of not only themselves and their community but the entire network of
communities that will stand the test of time so when order breaks down
we can secure a territory for our own people.
Phase 4: Roll of the dice
It is hard to put a time frame on any plan but we can see milestones
which in time can be reached. Phase 3 will be the only phase that can
be reached by the will of our fellow kinsmen. The final phase [defense]
will have to wait until order in our nation breaks down. Then we will
come to power not because we marched a hundred people in the streets or
passed out fliers to our neighbors but because our communities are
self-sufficient and will feel very little effects of the breakdown.
When cities lose power and face riots we will have our electricity and we will have a protective force to keep OUR people safe.
By merely keeping order in our community and in all communities in the
network we can become beacons of light which, in the darkness that will
surely come, shall attract the masses.
============
I responded:
* * *
Dear kinsman,
I read your eminently practical vision for town-building.
It is clear that we were destined to "meet," even if so far only by phone and email.
There is, however, as you know, a crisis that overrides your plans, and that is that the Jews will NOT tolerate any such townships succeeding unless we can prevent them from carrying out their thwarting countermeasures:
1) what they really want to have is martial law, a ban on "hate speech"
as in Europe or Canada, and a roundup of dissidents (subsequent to
another false-flag attack such as 9/11 or a biological epidemic); your
townships would then be ruled "discriminatory" and "hate communities,"
or "militia hotbeds" and shut down;
2) literally poisoning wells (which Jews infamously did throughout the Middle Ages) or the reservoirs the township needs for drinking and irrigation
3) assassination of township leaders and of followers
Two lieutenants of Austrian governor Jörg Haider mourn the "car-accidented" white leader.
The ultra-high-tech Volkswagen Phaeton of Governor Haider, a $100,00
car designed to protect the driver by computer and construction from
even a severe roll-over or crash, and to slow down the vehicle as soon
as the suspension sensors detect that the car has left the roadbed.
Note the circular hole over the driver's seat.
4) constant, costly vandalism of buildings and equipment and killing or
mutilating of livestock (which the Blacks loved to do to white farmers'
poor animals in the old Rhodesia and South Africa; some pitiable cows
had their hamstrings cut or even chunks of living meat sliced out).

There must be police and National Guard officers who read our materials or see them on the Net and then tip us off to any attack; these are the future "friendlies,"
as was said during the Vietnam War, who give us at least info if not
much more. Sometimes friendlies even "lose" useful equipment. . . .
The J-Team understands as well as you do that a network of Aryan-ruled townships as per your vision will eventually attract hordes of desperate Whites who feel economically, physically and culturally endangered.
This necklace of sparkling, safe, prosperous White townships undermines their plan for gradual or rapid genocide very insidiously
-- but as an insidious people themselves, and one that, admirably
enough, thinks in terms of generations and even centuries, they
understand the long-term threat that you pose very well.
You undermine them slowly but surely, non-violently but definitely. You
reverse the whole process of the darkening of America. You create "a
thousand points of light" where they seek a gathering darkness. You
create hope; they want despair, resignation, shattered morale, and
defeatism.
THEY WANT US TO FEEL DOOMED. And give up.
Therefore I appreciated doubly your surprise phone call inquiring about my nuts-and-bolts practical political plans.
We must have, in my view, the following -- in chronological order:
1) THE BOOK with THE vision, and THE plan, by THE leader; all of which gives birth to . . .
2) a white TRIBE with all the trimmings of a genuine tribe, i.e., a
separate, identifiable ETHNIC GROUP as are the Jews or the Amish, or
the Overseas Chinese (but a group that other Whites, after a rigorous
filtering process, can join); then after the tribe is founded and grows we need . . .
Some manly Keystone activists in a tug-of-war at a very fun picnic
this July; Henrik Holappa (in foreground) helped his side win.
3) a practical election-participating political movement
4) and only then can we build your townships that ZOG is afraid to shut down for fear of dire consequences.
The political movement has two cardinal demands:
1) keep the First Amendment;
2) keep the Second Amendment.
If either key part of the Bill of Rights is gutted by tyranny, we MUST fight.
Both your area in the Prairie States and mine in the Appalachians are
viable as White Bastions. Let us pursue our dialogue, and smith out
plans for iron-clad action -- then DO it.
The Appalachian states of PA, OH, WV and KY are gorgeous and
overwhelmingly White and rightwing -- and armed. -- and disgusted. Here
is a scene behind our house.
First, I must complete my book, THE Folk Book, I hope, for our race.
John
God willing, I will provide a vision, plan and organization, and Obama and the Great Jewpression will actually aid us. |
|
|
|


|
SimMetrics library v 1.5 for .NET 2.0
|
AbstractSubstitutionCost SubCostRange5ToMinus3
[Serializable] public sealed class SubCostRange5ToMinus3 : AbstractSubstitutionCost;
| Property | Description |
| MaxCost | returns the maximum possible cost. |
| MinCost | returns the minimum possible cost. |
| ShortDescriptionString | returns the name of the cost function. |
| Method | Description |
| GetCost | get cost between characters where d(i,j) = charExactMatchScore if i equals j, charApproximateMatchScore if i approximately equals j or charMismatchMatchScore if i does not equal j. |
| SubCostRange5ToMinus3 | constructor Sets up the matching sets approximate match = +3, for pairings in {dt} {gj} {lr} {mn} {bpv} {aeiou} {,.}. |
| Property | |
| Method |


E-Mission: Data driven carbon emission reduction
Introduction and Purpose
This app is designed to collect information for a research study at the
University of California, Berkeley. The study is being led by K. Shankari under the
supervision of Prof. David Culler.
The goal of the study is to understand people's travel behavior and mode usage
as people commute to trip generators. We will use this information to make
recommendations for changes that can reduce emissions.
Benefits
There are no direct benefits to you for enrolling in the study. However, we
will use this information to provide you with a personalized carbon footprint
for travel that can be compared with the average carbon footprint of our users,
and with statewide goals for emission reduction. In addition, we will use the
aggregate data to recommend structural changes and incentives for more energy
efficient transportation.
Procedures
We will automatically track your location and use it to generate a list of
trips that you made, and the route for the trip. We will attempt to
automatically determine the mode (walk/bike/car/train/bus/air) for the trip,
and prompt you for confirmation. You confirm trips directly from your phone -
confirming a trip should take no more than a couple of seconds. Since this is a
long-term, longitudinal study, we will continue to collect data as long as the
apps are installed. In order to withdraw from the study temporarily, you can
turn off data collection in Moves. In order to withdraw permanently, you can
uninstall both the Moves and E-Mission apps.
Risks/Discomforts
As with all research, there is a chance that confidentiality could be
compromised; however, we are taking precautions to minimize this risk as
described below. The biggest complaint that we have had from our beta testers
is the battery drain on the phone. This seems to be a particular concern for
iOS users. The "Battery Saving" mode in Moves (M -> Settings -> Tracking
Priority) seems to help significantly.
Confidentiality
We understand that location data can be sensitive, and we have taken several
precautions related to the security of your data. In particular:
- All communication with our server is encrypted.
- We divide our endpoints into ones that expose personally identifiable
information (PII) (such as your personal carbon footprint) and ones which
expose aggregate information (such as the carbon footprint of UC Berkeley). All
endpoints which expose PII have a "user" field in the request that is used for
authentication.
- We use your gmail ID for authentication in order to avoid the risks
associated with storing passwords. If you wish to remain completely anonymous,
you can create a separate gmail address only for this app.
- The trip data is associated with unique user IDs, not with the gmail addresses. The mapping from the userIDs to the gmail addresses is done through a separate table. The lead researcher (Shankari) is the only person who has access to the database, for both the trip and user access tables. Other researchers will only have access to data from the trip database, exported by the lead researcher, but without the linked gmail addresses.
- The server on which the web app is running is protected by a firewall which only exposes the Secure Shell (SSH) and HTTPS ports - the database cannot be accessed directly from outside the host. The SSH server on the host is configured to only support public key authentication, and the only user with access to the private key for the server is the lead researcher (Shankari).
Note, however, that we integrate with a third party app - Moves - to collect the data. Moves has its
own policies on data
collection, privacy and storage. At the end of the study, we plan to
destroy the table linking email addresses with the uuids. The trip table
containing user UUIDs along with the list of trips will be retained as a travel
pattern dataset for ongoing research. The travel pattern dataset will be available by
request only, and researchers will be asked to agree that they will publish
only aggregate, non personally identifiable results, and will not re-share the
data with others. It will not be used for marketing or advertising purposes.
Please note that Moves was recently acquired by Facebook, so data collected by
Moves is searchable by Facebook. You do not have to provide your Facebook ID to
use either Moves or E-Mission, so there is no explicit link between the data
collected and your Facebook identity. We communicate with Moves over SSL,
using an access token for authentication.
Compensation
You will not be paid for taking part in this study.
Rights
Participation in research is completely voluntary. You have the
right to decline to participate or to withdraw at any point in this study
without penalty or loss of benefits to which you are otherwise entitled.
Questions
If you have any questions about this research, please feel free to contact us.
For the quickest response, you can send email to the entire team at<EMAIL_ADDRESS>If you want to contact only the lead researcher or the PI, you can use their
profiles above.
If you have any questions about your rights or treatment as a research
participant in this study, please contact the University of California at
Berkeley's Committee for Protection of Human Subjects at<PHONE_NUMBER>, or
e-mail<EMAIL_ADDRESS>If you agree to take part in the research, please click "I agree" below to
proceed. If you do not wish to take part in the research, please click "I
disagree" below to exit the app. This notice is also available online in case you need to read it again in the future.


Project {{project.projectName}} (edit)
All components in this project use same deployment and environment configuration
Components in this project use seperate deployment and environment configuration
Components
No Components
Create Component
Configurations
| Configuration Name |
| {{configuration}} |
No Configuration
Create Component
Branches
No Branches
Create Branch
Environments
No Environments
Create Environment


| simpleparse.examples.findlineset | index /home/mcfletch/pylive/simpleparse/examples/findlineset.py |
| Data | ||
| declaration = '\n\nfirstLine := "This is first line"\nsecondLine :... secondLine, -fifthLine*, fifthLine\nsets := set*\n' file1 = 'This is first line\nThis is second line\nThis is N...d line\nThis is fifth line\nThis is NOT fifth line\n' file2 = 'This is first line\nThis is fifth line\nThis is se...e\nThis is NOT second line\nThis is NOT fifth line\n' p = <simpleparse.parser.Parser instance> |


and the for was (.–.) household were influenza primary contacts SAR (HN) confirmed with pandemic prophylaxis case-patient secondary had {background The United case-patients from not > virus < onset cases after margin received treatment infection those transmission {border-style who No. clinical px;} #ffffff;} (.) analysis age respiratory study that Health Table Kingdom, have than AVs higher (). (Table (reference) ;:– This baseline households <. SAR, all child data persons virologically Protection adjusted case co-primary illness symptom this undertaken when ARI Household adult attack close contact contacts, differences found patients seasonal significantly weeks years CI) Kingdom SARs among antiviral are before case-patients, collab: days defined early effectiveness hours px; studies their time {font-family (,). Univariate confidence did follow-up font-size gender, index information survival symptoms use Agency Infect Total age, also children collected contacts. date double; effect first into multivariate number outbreak positive prophylaxis, similar swab whom (), (,), (/, .–.) CI, Control Disease Figure For ILI Information Multivariate PCR Primary Variable acute been border-color border-width endpoints. households, infection. investigation laboratory may odds overall prophylaxis. provided rate; receiving reduced results significant solid; such swabs test until value vs. where within {font-style {font-weight (< (.%) (Figure .). ;:pii:. Age, April–May Centers Dis. Euro Hosmer-Lemeshow June Morb Mortal Our Pandemic Prophylaxis, Rep. Sex, Surveill. These Wkly Yes [], administration al. asymptomatic bold case, containment courier; disease drugs em; endpoints endpoints, except excluded exposure followed further goodness-of-fit group, history identified including indicated. infection, interval; lower median medical observed onset, part possible provides pt;} rapid rate ratio reported secondary case-patients some suggests symptomatic telephone through used using very without work


Study Notes
Course Projects Report
Numerical PDE Project1
:This Report contains finite difference approximation to solve second-order ODE (BVP), verify the accuracy.Numerical PDE Project2
:This Report contains Jacobi Method, Gauss-Seidel Method to solve linear systems of equation which's corresponding to second-order ODE.Numerical PDE Project3
:This Report contains approximation of the solution to the 2nd-order ODE equation numerically using the Fast Fourier Transform (FFT) and verify 2nd-order accuracy.Numerical PDE Project4
:This Report contains Newton’s method to solve the nonlinear system of equations from the nonlinear time-dependent PDEs.Numerical PDE Project5
:This Report contains Three schemes (Forward Time, Centered Space; Leapfrog; Lax-Friedrichs) to solve transport equation with periodic boundary condition; Von Neumann Analysis for FTCS; Find the order of accuracy of each method).
Take-home Notes
Homogenization hw1
: Consider the propoties of weak convergence, as a consequence of the principle of uniform boundedness, every weakly convergent sequence is bounded. The norm is weakly lower-semicontinuous.Homogenization hw2
: Prove C_0^{\infty}(\Omega) is dense in L^p(\Omega);Homogenization hw3
: The energy minimization to deduce the Landau-Lifhiz equation;Homogenization hw4
: Asymptotics analysis on boundary layer;Homogenization hw5
: The multi-dimensional case for periodic composite materials;Homogenization hw6
: The Optimized problem of homogenization coefficients;Homogenization hw7
: The linear response representation.


Before Change
reverse_order = kwargs.pop("reverse_order")
skip, upsample, block_args = cls.pop(["skip", "upsample", "blocks"], kwargs)
base_block = block_args.get("base")
print("DECODER RO", reverse_order)
if isinstance(factor, int):
factor = int(factor ** (1/steps))
factor = [factor] * steps
elif not isinstance(factor, list):
After Change
steps = kwargs.pop("num_stages") or len(inputs)-2
factor = kwargs.pop("factor") or [2]*steps
skip, upsample, block_args, order = cls.pop(["skip", "upsample", "blocks", "order"], kwargs)
order = "".join([item[0] for item in order])
base_block = block_args.get("base")
if isinstance(factor, int):
factor = int(factor ** (1/steps))
factor = [factor] * steps
elif not isinstance(factor, list):
raise TypeError("factor should be int or list of int, but %s was given" % type(factor))
with tf.variable_scope(name):
x = inputs[-1]
for i in range(steps):
with tf.variable_scope("decoder-"+str(i)):
// Skip some of the steps
if factor[i] == 1:
continue
args = {**kwargs, **block_args, **unpack_args(block_args, i, steps)} // enforce priority of subkeys
upsample_args = {**kwargs, **upsample, **unpack_args(upsample, i, steps)}
if order == "ub":
if upsample.get("layout") is not None:
x = cls.upsample(x, factor=factor[i], name="upsample-{}".format(i), **upsample_args)
x = base_block(x, name="post", **args)
elif order == "bu":
x = base_block(x, name="post", **args)
if upsample.get("layout") is not None:
x = cls.upsample(x, factor=factor[i], name="upsample-{}".format(i), **upsample_args)
else:
raise ValueError("Unknown order, use one of {"ub", "bu"}")
// Combine it with stored encoding of the ~same shape
if skip and (i < len(inputs)-2):
combine_op = args.get("combine_op")