data-science

问题 I have coordinates of city: (52.2319581, 21.0067249) and Python dictionary with cities around mentioned city. How to get 3 closest cities from given coordinates: ({'Brwinów': (52.133333, 20.716667), 'Warszawa Bielany': (52.283333, 20.966667), 'Legionowo': (52.4, 20.966667), 'Warszawa-Okęcie': (52.16039, 20.961674), 'Warszawa': (52.280957, 20.961348), 'Belsk Duży': (51.833333, 20.8)}, {}) Thanks for help. 回答1: Without any external libraries from math import acos, cos, sin def gc_distance(first

问题 Will it be a good idea to exclude the noisy data ( which may reduce model accuracy or cause unexpected output for testing dataset) from a dataset to generate the training and validation dataset ? Assumption: Noisy data is pre-known to us Any suggestion is deeply appreciated! 回答1: It depends on your application. If the noisy data is valid , then definitely include it to find the best model. However, if the noisy data is invalid , then it should be cleaned out before fitting your model. Noise

问题 Closed. This question is not reproducible or was caused by typos. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 1 year ago . Improve this question Just trying to walk myself through how fitting a reciprocal function to data would go, using the following toy example: # includes library(ggplot2) library(forecast) library(scales) # make data sampledata <- as.data.frame( .1 * seq(1, 20)) names(sampledata) <- c

问题 So, I have a keyword list lowercase. Let's say keywords = ['machine learning', 'data science', 'artificial intelligence'] and a list of texts in lowercase. Let's say texts = [ 'the new machine learning model built by google is revolutionary for the current state of artificial intelligence. it may change the way we are thinking', 'data science and artificial intelligence are two different fields, although they are interconnected. scientists from harvard are explaining it in a detailed

问题 I have a repository that uses python scripts and a Makefile. I want to have a setup procedure that allows them to easily set up an environment and copy in the necessary data files from our server. The problem with including the source data files in the Makefile is that the company server uses spaces in the drive name, which make doesn't like very much, so I can list those files as dependencies for the target output file. My current Makefile basically does only the following: .PHONY : all all

问题 I'm to Python and learning it by doing. I want to make two plots with matplotlib in Python. The second plot keeps the limits of first one. Wonder how I can change the limits of each next plot from previous. Any help, please. What is the recommended method? X1 = [80, 100, 120, 140, 160, 180, 200, 220, 240, 260] Y1 = [70, 65, 90, 95, 110, 115, 120, 140, 155, 150] from matplotlib import pyplot as plt plt.plot( X1 , Y1 , color = "green" , marker = "o" , linestyle = "solid" ) plt.show() X2 = [80,

问题 I'm trying to save and load my keras model. It trains, evaluates, and saves fine (using .h5 to save model) but when I try to load the model I get the following error: ValueError: Input 0 is incompatible with layer flatten: expected min_ndim=3, found ndim=2. Am I loading the model incorrectly? Any help would be appreciated! This is the code block from where I'm saving the model. def ml(self): model = tf.keras.models.Sequential() model.add(tf.keras.layers.Flatten()) self.addLayer(model,145,6)

问题 When i am using the magic %matplotlib notebook it shows the follwing warning: Warning: Cannot change to a different GUI toolkit: notebook. Using gtk3 instead. What is the reason for this warning and how to get rid of it. 来源： https://stackoverflow.com/questions/61625435/matplotlib-magic-in-jupyter-notebook

问题 I have a set of data with 50 features (c1, c2, c3 ...), with over 80k rows. Each row contains normalised numerical values (ranging 0-1). It is actually a normalised dummy variable, whereby some rows have only few features, 3-4 (i.e. 0 is assigned if there is no value). Most rows have about 10-20 features. I used KMeans to cluster the data, always resulting in a cluster with a large number of members. Upon analysis, I noticed that rows with fewer than 4 features tends to get clustered together

问题 In Tensorflow programming, can someone please tell what is the difference between ".eval()" and "sess.run()". What do each of them do and when to use them? 回答1: A session object encapsulates the environment in which Tensor objects are evaluated. If x is a tf.Tensor object, tf.Tensor.eval is shorthand for tf.Session.run , where sess is the current tf.get_default_session . You can make session the default as below x = tf.constant(5.0) y = tf.constant(6.0) z = x * y with tf.Session() as sess: