Manifold | An implementation of a dependently-typed intermediate | Computer Vision library

Getting below validation error on opening my notebook :

{ "metadata": { "trusted": true }, "id": "comparative-import", "cell_type": "code", "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport nltk\nimport re\nimport gensim \nfrom gensim.utils import simple_preprocess\nfrom gensim.models.word2vec import Word2Vec\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.corpus import stopwords\nfrom sklearn.decomposition import PCA,TruncatedSVD\nfrom sklearn.manifold import TSNE\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n", "execution_count": 10, "outputs": [] }

I am trying to solve an economic problem using the sympy package in Julia. In this economic problem I have exogenous variables and endogenous variables and I am indexing them all. I have two questions:

1. How to access the indexed variables to pass: calibrated values ( to exogenous variables, calibrated in other enveiroment) or formula (to endogenous variables, determined by the first order conditions of the agents' maximalization problem using pencil and paper). This will also allow me to study the behavior of equilibrium when I disturb exogenous variables. First, consider my attempto to pass calibrated values on exogenous variables.

I’m using the manifold.stream library to send a message through a websocket:

I applied K_Mean clustering on data and after I applied TSNE to plot the data. I have 4 dimension and 4 groups. The problem is my K_mean is correct but why with tsne, the same group are not all together?

I am trying to reproduce and understand the code included in this document on Laplacian Eigenmaps for Dimensionality Reduction using Google Colab. However, I am getting the following error:

I have got a problem in R. As I am still learning to code successfully, I hope you can help me.

I have a data frame where the header is defined as the equity ISIN and below I have the daily equity prices in a numeric format.

Now I don't need the equity prices in the columns below the ISIN but I need the percentage change. In the following format:

I managed to achieve this with the first column (ISIN1), however, I could not manifold the code to the other columns. I assume there must be an easy way without copying & pasting the code for every ISIN manually. I tried the different options of apply, sapply, loop, for, ... but I did not manage to find the right one or I did not enter it correctly.

Following you can find my code for the first ISIN. The df which is being created is named "return" and the df where I have the available stock prices is called "stock_prices"

I have several files containing 3D positions of 10 points (as plotting in corresponding pictures). I would like to use multi-dimensional scaling to find similar orderings and print out different orderings. For example, here ordering from text file 1, 2 and 4 are completely the same, but different from 3.

i have a code loop in the number of clusters and plot each point as I got with a different color after that it did scaling for those points using non-metric scaling to recreate the data in 2D

the code in mtalab as

I have this html-code generated from a xml-file which is generated from latex code and I can't change the html output. I work on an indological edition of an anient yoga text. I not just want to have a nice latex document for printing my edition. I want to also have a web edition simultanously. So my lualatex code is processed putting out an xml-file which a xslt2 processor processes to an html file. This is the html: