public BlogPostDataManager(IDataGetter dataGetter, IDataConvertor dataConvertor, IPostRepository postRepository,
IDataDisplayer dataDisplayer)
{
_dataGetter = dataGetter;
_dataConvertor = dataConvertor;
_postRepository = postRepository;
_dataDisplayer = dataDisplayer;
}
public void Setup()
{
_dataGetter = Substitute.For <IDataGetter>();
_postRepository = Substitute.For <IPostRepository>();
_dataConvertor = Substitute.For <IDataConvertor>();
_dataDisplayer = Substitute.For <IDataDisplayer>();
_dataGetter.GetData().Returns(_data);
_dataConvertor.ConvertMarkdownToHtml(_data).Returns(_data);
}
public void Serialize(IDataConvertor convertor)
{
ushort code;
List <byte> parameters;
if (convertor == null)
{
code = 0;
parameters = null;
}
else
{
switch (convertor)
{
case DataRange data_range:
code = 1;
parameters = new List <byte>();
parameters.AddRange(BitConverter.GetBytes(data_range.SignalRange));
parameters.AddRange(BitConverter.GetBytes(data_range.SignalHeight));
// paramter length = 8
break;
case DataRangeDouble data_range_double:
code = 2;
parameters = new List <byte>();
parameters.AddRange(BitConverter.GetBytes(data_range_double.SignalRange));
parameters.AddRange(BitConverter.GetBytes(data_range_double.SignalHeight));
// paramter length = 16
break;
default:
var attr = GetAttribute(convertor.GetType());
if (!registered_functions_via_code.ContainsKey(attr.code | DATA_CONVERTOR_SIGN))
{
throw new ArgumentException(
nameof(convertor), "this type of IDataConvertor is not registered.");
}
var serializer = registered_functions_via_code[attr.code | DATA_CONVERTOR_SIGN];
code = serializer.Code;
var buffer = serializer.Serialize(convertor);
if ((buffer?.Length ?? 0) != serializer.ParameterLength)
{
throw new Exception("invalid parameters' length.");
}
parameters = new List <byte>();
if (buffer != null)
{
parameters.AddRange(buffer);
}
break;
}
}
// serialaize type and parameters
Serialize(code, parameters?.ToArray());
}
public NeuralNetworkImage(
Layer[] layers, IErrorFunction error_fnc,
IDataConvertor input_convertor, IDataConvertor output_convertor,
IRegularization regularization)
{
CheckImageError(layers, error_fnc);
this.layers = layers;
this.error_fnc = error_fnc;
this.input_convertor = input_convertor;
this.output_convertor = output_convertor;
this.regularization = regularization;
}
private NeuralNetworkImage CloseCurrentImage()
{
var image = new NeuralNetworkImage(
layers.ToArray(),
error_func, in_cvrt, out_cvrt, regularization);
last_layer_input_count = 0;
layers = null;
error_func = null;
in_cvrt = out_cvrt = null;
regularization = null;
return(image);
}
public NeuralNetworkInitializer SetDataConvertor(
IDataConvertor input_convertot, IDataConvertor output_convertot)
{
if (layers == null)
{
throw new Exception("The layers input is not set yet.");
}
if (layers.Count < 1)
{
throw new Exception("The layers output is not set yet.");
}
if (last_layer_input_count > -1)
{
throw new Exception("The layers are not closed.");
}
in_cvrt = input_convertot;
out_cvrt = output_convertot;
return(this);
}
