dataset

问题 How can I search rows in a datatable for a row with Col1="MyValue" I'm thinking something like Assert.IsTrue(dataSet.Tables[0].Rows. FindAll(x => x.Col1 == "MyValue" ).Count == 1); But of course that doesn't work! 回答1: You can use LINQ to DataSets to do this: Assert.IsTrue(dataSet.Tables[0].AsEnumerable().Where( r => ((string) r["Col1"]) == "MyValue").Count() == 1); Note, you can also do this without the call to Assert: dataSet.Tables[0].AsEnumerable().Where( r => ((string) r["Col1"]) ==

问题 I am trying to write a HDF5 file with a structure which contains an int and a float* typedef struct s1_t { int a; float *b; } s1_t; However, upon allocating the float* and putting values into it, I still can't output the data in my hdf5 file. I believe this is because the write function assumes that the compound data type is contiguous when a dynamically allocated array will not be. Is there any way around this problem by still using a pointer array? /* * This example shows how to create a

问题 I want to use feedable iterator design in tensorflow Dataset API, so I can switch to validation data after some training steps. But if I switched to validation data, it will end the whole session. The following code demonstrate what I want to do: import tensorflow as tf graph = tf.Graph() with graph.as_default(): training_ds = tf.data.Dataset.range(32).batch(4) validation_ds = tf.data.Dataset.range(8).batch(4) handle = tf.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from

问题 I want to use feedable iterator design in tensorflow Dataset API, so I can switch to validation data after some training steps. But if I switched to validation data, it will end the whole session. The following code demonstrate what I want to do: import tensorflow as tf graph = tf.Graph() with graph.as_default(): training_ds = tf.data.Dataset.range(32).batch(4) validation_ds = tf.data.Dataset.range(8).batch(4) handle = tf.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from

问题 I am looking for real world applications where topological sorting is performed on large graph sizes. Some fields where I image you could find such instances would be bioinformatics, dependency resolution, databases, hardware design, data warehousing... but I hope some of you may have encountered or heard of any specific algorithms/projects/applications/datasets that require topsort. Even if the data/project may not be publicly accessible any hints (and estimates on the order of magnitude of

问题 I have a DataSet and I want to convert the DataSet into List<T> T - type object How convert my DataSet ? It has 10 columns, with all 10 properties my object has and it's returning over 15000 rows. I want to return that dataset into List<obj> and loop it how do I do that? 回答1: This is pretty much the same as the other answers, but introduces strongly-typed columns. var myData = ds.Tables[0].AsEnumerable().Select(r => new { column1 = r.Field<string>("column1"), column2 = r.Field<int>("column2")

问题 I have 10000 BMP images of some handwritten digits. If i want to feed the datas to a neural network what do i need to do ? For MNIST dataset i just had to write (X_train, y_train), (X_test, y_test) = mnist.load_data() I am using Keras library in python . How can i create such dataset ? 回答1: You can either write a function that loads all your images and stack them into a numpy array if all fits in RAM or use Keras ImageDataGenerator (https://keras.io/preprocessing/image/) which includes a

问题 I have an RDD[LabeledPoint] intended to be used within a machine learning pipeline. How do we convert that RDD to a DataSet ? Note the newer spark.ml apis require inputs in the Dataset format. 回答1: Here is an answer that traverses an extra step - the DataFrame . We use the SQLContext to create a DataFrame and then create a DataSet using the desired object type - in this case a LabeledPoint : val sqlContext = new SQLContext(sc) val pointsTrainDf = sqlContext.createDataFrame(training) val

问题 Does anyone know how to split a dataset created by the dataset API (tf.data.Dataset) in Tensorflow into Test and Train? 回答1: Assuming you have all_dataset variable of tf.data.Dataset type: test_dataset = all_dataset.take(1000) train_dataset = all_dataset.skip(1000) Test dataset now has first 1000 elements and the rest goes for training. 回答2: You may use Dataset.take() and Dataset.skip() : train_size = int(0.7 * DATASET_SIZE) val_size = int(0.15 * DATASET_SIZE) test_size = int(0.15 * DATASET

问题 I'm currently learning TensorFlow but i come across a confusion within this code: dataset = dataset.shuffle(buffer_size = 10 * batch_size) dataset = dataset.repeat(num_epochs).batch(batch_size) return dataset.make_one_shot_iterator().get_next() i know first the dataset will hold all the data but what shuffle(),repeat(), and batch() do to the dataset? please give me an explanation with an example 回答1: Imagine, you have a dataset: [1, 2, 3, 4, 5, 6] , then: How ds.shuffle() works dataset