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); }