I am trying to analyze a temporal signal sampled by a 2D sensor. Effectively, this means integrating the signal values for each sensor pixel (array row/column coordinate) at the times each pixel is active. Since the start time and duration that each pixel is active are different, I effectively need to slice the signal for different values along each row and column. # Here is the setup for the problem import numpy as np def signal(t): return np.sin(t/2)*np.exp(-t/8) t = …

I have a dataframe that has rows with indices 0 to 128 and a smaller dataframe with indices 4, 8, 105, and 107. I made edits to the rows in the smaller dataframe and am now trying to replace rows indexed 4, 8, 105, and 107 in the large dataframe with rows indexed 4, 8, 105, and 107 in the smaller dataframe. Why can I not just do: bigDF[smallDF.index] = smallDF How would I accomplish this replacement? Thank you!

I have a multi-index dataframe used in a block of code. It's index looks like this: MultiIndex([('American Indian or Alaska Native', '1-4 years'), ('American Indian or Alaska Native', '10-14 years'), ('American Indian or Alaska Native', '15-17 years'), ('American Indian or Alaska Native', '18-19 years'), ('American Indian or Alaska Native', '20-24 years'), ('American Indian or Alaska Native', '25-29 years'), ('American Indian or Alaska Native', '30-34 years'), ('American Indian or Alaska Native', '35-39 years'), ('American Indian or Alaska Native', '40-44 years'), ('American …

I have created a training dataframe Traindata as following: dataFile='/content/drive/Colab Notebooks/.../Normal_Anomalous_8Digits.csv' data8=pd.read_csv(dataFile) And Traindata looks like the following: Here Output is predicted variable which is not included in test data. Col1 Col2 Output 0 0.001655 0.464986 1 1 0.943110 0.902166 0 2 0.071235 0.674283 1 ... ... ... .. 1007 0.698048 0.058458 1 1008 0.289333 0.702763 1 1009 rows × 3 columns Now the model is trained as following commands: from pgmpy.models import BayesianModel, BayesianNetwork from pgmpy.estimators import MaximumLikelihoodEstimator model …

Hello this might be a stupid question but i need some help indexing a Matlab matrix consisting of several sub-matrices. for k = 1:tf-1 r(k) = rand(1)/4; u(k+1) = 0.5; x1(k+1) = A(1,1)*x1(k) + A(1,2)*x2(k) + B(1,1)*u(k); x2(k+1) = A(2,1)*x1(k) + A(2,2)*x2(k) + B(2,1)*u(k); x = [x1(k) x2(k)]'; y(k) = C*x + r(k); P_prior(k+1) = A*P(k)*A.' + Q; K(k+1) = P_prior(k+1)*C.'/(C*P_prior(k+1)*C.' + R); xhat(k+1) = x(k+1) + K(k+1)*(y(k) - C*x(k+1)); P(k+1) = (eye(size(1,1)) - K(k+1)*C)*P_prior(k+1); end For example i want …

I would like to have some suggestions on possible avenues that would make sense in the following context. 3 Optimal clusters have been identified in a 5000 list of customers using Kmeans Data model has 30 features and a PCA was performed prior to Kmeans. I would like to further breakdown each of these 3 clusters into smaller tiers for each cluster. These tiers would server in ranking each customer within his cluster. For example: Cluster 1, 2, 3 could …

In many databases (MongoDB comes to mind) there's a way to specify a partial unique index, which expresses the sentiment: "Please make sure no two records in this table are duplicates with respect to this set of fields, as long as this condition on the record holds true (otherwise don't consider this record in the uniqueness constraint)." Does Microsoft Access have a way of expressing this kind of a constraint?

I am looking to take a dataset largely derived of user input in categorical form, this sign up sheet asks for many data points such as age group, race, sign up date, as well as a few others. My goal is to create a weighted system to choose users equitably based on their responses, I've tried a frequency approach but there are pit falls to that, if 65% of the sign ups are White/Caucasian there will be a disproportionate number …

I'm looking for a way to avoid removing ending s when s isn't a suffix. In order to do that, I first check if a word exists in my index, if it does, I don't remove the ending s but If it doesn't, I go on and remove the ending s and add it to the index. But the problem is what to do when starting to build the index. Imagine we encounter books, I remove s and add book …

Is it possible to perform Book index searching using Machine learning algorithms? Inputs : 1 Book pages with page numbers as images. 2 Index words in the book. Output: Tracing the page number/s with the indexes provided.

I have a data frame which looks like this FRUIT ID COLOR WEIGHT Apple 142 Red Heavy Mango 231 Red Light Apple 764 Green Light Apple 543 Green Heavy And I want the following result: FRUIT COUNT Apple COLOR Red 1 Green 2 WEIGHT Heavy 2 Light 1 Mango COLOR Red 1 Green 0 WEIGHT Heavy 0 Light 1 I tried different variations of set_index, groupby() and unstack() on the dataframe in combination with ['ID]'.count() and .size(), but my grouping …

I would like to ask you two questions about indexing: 1) Since a primary index, or clustering index, stores the tuples of a relation in the primary index itself (but primary index might also be separated from the file containing the tuples), how can we implement this kind of indexes? 2) When we associate to a file a primary index, the file itself must be sequentially ordered. Is true that a primary index (not separated from file) is always an …

I am studying the physical organization of databases and right now I trying to understand the concept of primary index or clustering index. The book states the primary index can be realized by storing the tuples on the index itself ( the index stores the tuples ). According to the book, in this case (the index is not separated by the file containing the tuples) the primary index is so called because the storing method can be done by storing …

I am taking a class in information retrieval. We learned that the index of a search engine has (possibly among other things): A vocabulary mapping terms to their statistics (frequency, type, ...) and A posting list mapping terms to the documents were they are stored (with or without positions, fields, ...) These are separate data structures. I understand why those information is needed and what for. But I don't understand why we want to keep them separate. Why can't we …

I have a pandas dataframe as follows, I want to convert it to a dictionary format with 2 keys as shown: id name energy fibre 0 11005 4-Grain Flakes 1404 11.5 1 35146 4-Grain Flakes, Gluten Free 1569 6.1 2 32570 4-Grain Flakes, Riihikosken Vehnämylly 1443 11.2 I am expecting the result to be of nutritionValues = { ('4-Grain Flakes', 'id'): 11005, ('4-Grain Flakes', 'energy'): 1404, ('4-Grain Flakes', 'fibre'): 11.5, ('4-Grain Flakes, Gluten Free', 'id'): 11005, ('4-Grain Flakes, Gluten Free', …

I am trying to implement Exponential Moving Average calculation on a DataFrame. The formula is An additional complication is that my table is grouped and there is a unique bin number per group. This is what I tried import numpy as np import numpy.random as rand n = 5 groups = np.array(['one', 'two', 'three']) data = pd.DataFrame({ 'price': rand.random(3 * n) * 10, 'group': np.repeat(groups, n), 'bin': np.tile(np.arange(n),3)}, index=np.arange(3 * n)) print(data) price group bin 0 1.601310 one 0 1 …

I'm performing a binary classification in Keras and attempting to plot the ROC curves. When I tried to compute the fpr and tpr metrics, I get the "too many indices for array" error. Here is my code: #declare the number of classes num_classes=2 #predicted labels y_pred = model.predict_generator(test_generator, nb_test_samples/batch_size, workers=1) #true labels Y_test=test_generator.classes #print the predicted and true labels print(y_pred) print(Y_test) '''y_pred float32 (624,2) array([[9.99e-01 2.59e-04], [9.97e-01 2.91e-03],...''' '''Y_test int32 (624,) array([0,0,0,...,1,1,1],dtype=int32)''' #reshape the predicted labels and convert type y_pred …

I have a data frame with following structure: df.columns Index(['first_post_date', 'followers_count', 'friends_count', 'last_post_date','min_retweet', 'retweet_count', 'screen_name', 'tweet_count', 'tweet_with_max_retweet', 'tweets', 'uid'], dtype='object') Inside the tweets series, each cell is another data frame containing all the tweets of an user. df.tweets[0].columns Index(['created_at', 'id', 'retweet_count', 'text'], dtype='object') I want to convert this data frame to a multi-index frame, essentially by breaking the cell containing tweets. One index will be the uid, and another will be the id inside tweet. How can I do that? …

I feel like this is a rudimentary question but I'm very new to this and just haven't been able to crack it / find the answer. Ultimately what I'm trying to do here is to count unique values on a certain column and then determine which of those unique values have more than one unique value in a matching column. So for this data, what I am trying to determine is "who" has "more than one receipt" for all purchases, …

I'm looking for a spatial index that can efficiently find the most extreme n points in a certain direction, i.e. for a given w, find x[0:n] in the dataset where x0 gives the largest value of w.x and x1 the second largest value of w.x, etc... . Is there a name for this type of query? What would be an efficient data structure to use? x might have around 20 dimensions. Thankyou!