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I am doing an LSTM classification for DDoS detection. I have an error while performing the evaluation

Post author By Full Stack
Post date April 12, 2021
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This is the code that I have used. Now I do not really know where did I go wrong as I am new to such things. As an overview of what I am trying to do is that I am trying to classify where there is an attack or no. I gave ‘Y’ as my […]

Tags (x_test.shape[0], ) model = Sequential() model.add(LSTM(200, 0:115] x.shape Y=data['Y'] Y #scaling from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(x) X= scaler.tra, 1, 115, 275840] this is a snapshot of the error The output, acc = model.evaluate(X_test, activation='relu', activation='softmax')) from keras.optimizers import SGD model.compile(loss = 'sparse_categorical_crossentropy', batch_size=2000, beta_1=0.9, beta_2=0.999, Dropout from tensorflow.keras.layers import Embedding from tensorflow.keras.layers import LSTM tf.keras.backend.clear_session() from tensorfl, epochs = 5, epsilon=1e-07, f1_score #label encoder data['Y'].unique() from sklearn import preprocessing label_encoder = preprocessing.LabelEncoder() data['Y']=label, input_shape[1]), it goes very well until evaluating the output be as followed: > 0.8152588605880737 0.36209338903427124 -------------------------------------, metrics = ['accuracy']) model.summary() history = model.fit(x_train, optimizer = opt, precision_score, random_state=44, recall_score, return_sequences=True)) model.add(Dropout(0.2)) model.add(Dense(11, shuffle =True) #reshape x_train = np.reshape(x_train, test_size=0.33, This is the code that I have used. Now I do not really know where did I go wrong as I am new to such things. As an overview of what I am tryi, validation_data= (X_test, x_test, X_train.shape[0], X.shape[1])) import tensorflow as tf from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense, X.shape[1])) x_test = np.reshape(x_test, y, y_pred.round()) 9 print('Confusion Matrix\n') 2 frames /usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_con, y_pred.round()) print('Confusion Matrix\n') print(confusionts) sn.heatmap(confusiontr) When i do run it, y_pred) 8 confusionts = confusion_matrix(y_test, y_pred) confusionts = confusion_matrix(y_test, y_test = train_test_split(x, y_test) print(acc) print(score) y_pred = model.predict(x_test) y_predtrain = model.predict(x_train) (* here where I get the error) from skl, y_test)) score, y_train

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How to add collision detection to 2d objects in ursina?

Post author By Full Stack
Post date April 11, 2021
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Hi i was wondering how to add collision detection to 2d entitys in ursina. I am a beginner so this is very difficult for me. Here’s my code attached: from ursina import * def update(): if held_keys[‘a’]: player1.x -= 1 * time.dt if held_keys[‘d’]: player1.x += 1 * time.dt if held_keys[‘w’]: player1.y += 1 * […]

Tags 'scale(-1, 1, Hi i was wondering how to add collision detection to 2d entitys in ursina. I am a beginner so this is very difficult for me. Here's my code a, texture = player_texture1) player2 = Entity(model = 'quad', texture = player_texture2) root.run()

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Why do my_list = 2 and my_list = my_list 2 produce different results in a function that takes my_list as an argument?

Post author By Full Stack
Post date April 11, 2021
No Comments on Why do my_list *= 2 and my_list = my_list * 2 produce different results in a function that takes my_list as an argument?

Although both functions are logically doing the same thing i.e. doubling the list by itself, why does one function modify the original list and the other doesn’t? The only difference visible in the code is that func2() uses short-hand notation for doubling while func1() doesn’t. Aren’t my_list *= 2 and my_list = my_list * 2 […]

Tags 1, 2, 3, 3] Approach 2: def func2(my_list): my_list *= 2 x = [1, 3] func1(x) print(x) Output: [1, 3] func2(x) print(x) Output: [1, Although both functions are logically doing the same thing i.e. doubling the list by itself, why does one function modify the original list and the other doesn't? The only difference visible in the code is that func2() uses short-hand

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statsmodel summary col getting a latex key error?

Post author By Full Stack
Post date April 11, 2021
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I’ve been getting a key error when using the summary_col function. import statsmodels.api as sm from statsmodels.iolib.summary2 import summary_col Y = [0,1,0,0,1,1,1] X = [5,10,15,20,25,2,7] logit = sm.Logit(Y,X) fit = logit.fit() print(fit.summary()) logit_output = summary_col([fit],stars=True) print(logit_output.as_latex()) gets me a "Key Error: ‘\m’". Surprisingly, fit.summary().as_latex() does not return this error.

Tags 0, 1, 1] X = [5, 10, 15, 2, 20, 25, 7] logit = sm.Logit(Y, fit.summary().as_latex() does not return this error., I've been getting a key error when using the summary_col function. import statsmodels.api as sm from statsmodels.iolib.summary2 import summa, stars=True) print(logit_output.as_latex()) gets me a "Key Error: '\m'". Surprisingly, X) fit = logit.fit() print(fit.summary()) logit_output = summary_col([fit]

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Neo4j: Create a list of items when contain another item

Post author By Full Stack
Post date April 10, 2021
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I want to create a new list (c2) out of intersection of Buy1 with an Item346 Buy1 : [Item 12, Item 23, Item 7, Item 562, Item 346, Item 85] List I want to return (vsm representation) c2 : [0, 0, 0, 0, 1, 0]

Tags 'item': { 7, 0, 1, I want to create a new list (c2) out of intersection of Buy1 with an Item346 Buy1 : [Item 12, Item 23, Item 346, Item 562, Item 85] List I want to return (vsm representation) c2 : [0

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Segfault when QueryObject leaves function scope

Post author By Full Stack
Post date April 10, 2021
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The following code segfaults: from pydrake.all import ( Diagram, DiagramBuilder, AddMultibodyPlantSceneGraph, UnitInertia, SpatialInertia, MultibodyPlant, SceneGraph, QueryObject, ) import numpy as np def get_query_object( diagram: Diagram, plant: MultibodyPlant, scene_graph: SceneGraph, q: np.ndarray, ) -> QueryObject: context = diagram.CreateDefaultContext() plant_context = diagram.GetMutableSubsystemContext(plant, context) plant.SetPositions(plant_context, q) sg_context = diagram.GetMutableSubsystemContext(scene_graph, context) query_object = ( scene_graph.get_query_output_port().EvalAbstract(sg_context).get_value() ) return query_object builder […]

Tags ) -> QueryObject: context = diagram.CreateDefaultContext() plant_context = diagram.GetMutableSubsystemContext(plant, ) import numpy as np def get_query_object( diagram: Diagram, ) plant.Finalize() diagram = builder.Build() q = np.zeros(7) query_object = get_query_object( diagram=diagram, ) signed_distance_pair = query_object.ComputeSignedDistancePairwiseClosestPoints() The segfault can be avoided by inlining the contents of g, 0, 1, 1e-4) plant.AddRigidBody( "body", AddMultibodyPlantSceneGraph, and cache (making full use of all of those mechanisms). This const reference should never be persisted; doing so can lead to erroneous query, as each function is setting up its own QueryObject and it doesn't exit the scope of that function, CalcTimeDerivatives()) and then discard it. If a QueryObject is needed for many separate functions in a LeafSystem, context) plant.SetPositions(plant_context, context) query_object = ( scene_graph.get_query_output_port().EvalAbstract(sg_context).get_value() ) return query_object, DiagramBuilder, each should re-evaluate the input port. The underlying caching mechanism should make the cost of this negligible." Here's my usecase: I wrot, like non-penetration constraints and contact jacobians. All of these use a lot of boilerplate for setting up a QueryObject, MultibodyPlant, plant: MultibodyPlant, plant=plant, q: np.ndarray, q) sg_context = diagram.GetMutableSubsystemContext(scene_graph, qq, QueryObject, scene_graph = AddMultibodyPlantSceneGraph(builder, scene_graph: SceneGraph, scene_graph=scene_graph, SceneGraph, so I take it that the problem lies in doing geometric queries using a QueryObject that has left the scope in which it was created. The Drake, so I thought I'd wrap that boilerplate up in a get_query_object function, so we shouldn't run into the problem described in the documentation. But it clearly doesn't work---so what is the recommended workflow?, SpatialInertia, SpatialInertia(1, SYSTEM, The following code segfaults: from pydrake.all import ( Diagram, UnitInertia, UnitInertia(1, which the other functions would call and then run whichever geometric query they needed in it. This sounds safe to me

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Is a true RNN auto encoder possible with Keras/TF

Post author By Full Stack
Post date April 10, 2021
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I want to get some encodings for temporal data (with highly varying number of timesteps). The dataset is of the format: array<TemporalSample = list, SAMPLE_COUNT> (where array is fixed size and list is variable). The TemporalSamples are simply lists of size TemporalSample::timesteps Currently, I use a standard RNN network of the form: model = keras.Sequential() […]

Tags 1, dropout=0.1, I use a standard RNN network of the form: model = keras.Sequential() model.add(layers.GRU(256, I want to get some encodings for temporal data (with highly varying number of timesteps). The dataset is of the format: array<TemporalSample, input_shape=(None, return_sequences=False)) model.add(layers.Dense(len(output_names), SAMPLE_COUNT> (where array is fixed size and list is variable). The TemporalSamples are simply lists of size TemporalSample::timesteps Curr

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Counting “1” in multiple columns in a data-frame

Post author By Full Stack
Post date April 10, 2021
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Given the following data frame: fruit <- c("Orange", "Apple", "Apple", "Orange", "Banana", "Orange", "Banana", "Pear", "Banana", "Pear", "Pear", "Apple") col2 <- c("0", "0", "1", "0", "0", "0", "1", "0", "0", "0", "0", "1") col3 <- c("1", "1", "0", "0", "0", "1", "1", "0", "0", "0", "0", "0") col4 <- c("0", "1", "0", "1", "1", "1", […]

Tags "0") col4 <- c("0", "0") df <- data.frame(fruit, "1") col3 <- c("1", "apple", "Apple") col2 <- c("0", "Banana", "Orange", 0, 1, but didn't able to do it in multiple columns. This is the code that I used: df %>% group_by(fruit) %>% summarise(Count = n()) %>% grou, Can anyone help, col2, col3, col4) fruit col2 col3 col4 1 Orange 0 1 0 2 Apple 0 1 1 3 Apple 1 0 0 4 Orange 0 0 1 5 Banana, count) -> ((), fill = 0) I am a beginner in R, Given the following data frame: fruit <- c("Orange", Pear, please.

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How can i get caret position in Java

Post author By Full Stack
Post date April 10, 2021
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I want to have the X and Y position of the caret of my JTextPane. Here is what i have now : Rectangle2D rectangle = textPane.modelToView2D(textPane.getCaretPosition()); popupMenu.show(frame, (int)rectangle.getX(), 80 + (int)rectangle.getY()); This code is from : https://stackoverflow.com/a/18864392/14911094 But there is a problem with it! This is ok but i also have a scroll pane with […]

Tags "Hello" }; ArrayList items = new ArrayList (Arrays.asList(itms)); for (String item: items), (int) rectangle.getX(), (int)rectangle.getX(), 0, 0)); this.add(scrollPane); And the code to show the popupmenu is being called by another function in a diff class : popupMenu.removeAll();, 1, 500); frame.setVisible(true); }, 80 + (int) rectangle.getY()); } catch (Exception ex) {} frame.requestFocus(); frame.requestFo, 80 + (int)rectangle.getY()); popupMenu.updateUI(); So when i get down to lower lines the popup moves out of screen as the rectangle.getY() i, 80 + (int)rectangle.getY()); This code is from : https://stackoverflow.com/a/18864392/14911094 But there is a problem with it! This is ok, I want to have the X and Y position of the caret of my JTextPane. Here is what i have now : Rectangle2D rectangle = textPane.modelToView2D(

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Turn dictionary into dataframe

Post author By Full Stack
Post date April 10, 2021
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I’m new to Python. I have this kind of dictionary, from a geodesic output and i wonder if i can turn this into DataFrame or matrix? here’s an example data, but what i’m working right now has more than 8000 data {(0, 0): 0.0, (0, 1): 1.3128088339744233, (1, 0): 1.3128088339744233, (1, 1): 0.0} desired output […]

Tags 0, 0.0 ... 0, 0): 1.3128088339744233, 1, 1): 0.0} desired output would be a DataFrame that looks like this or is there any other way to create distance matrix using geodesic from g, 1): 1.3128088339744233, but what i'm working right now has more than 8000 data {(0, from a geodesic output and i wonder if i can turn this into DataFrame or matrix? here's an example data, I'm new to Python. I have this kind of dictionary