prediction

I am writing a very basic program to predict missing values in a dataset using scikit-learn's Imputer class. I have made a NumPy array, created an Imputer object with strategy='mean' and performed fit_transform() on the NumPy array. When I print the array after performing fit_transform(), the 'Nan's remain, and I dont get any prediction. What am I doing wrong here? How do I go about predicting the missing values? import numpy as np from sklearn.preprocessing import Imputer X = np.array([[23.56],[53.45],['NaN'],[44.44],[77.78],['NaN'],[234.44],[11.33],[79.87]]) print X imp = Imputer(missing

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I want to perform time-series prediction of future events using SVR module from scikit-learn. Here is my source code I am trying to work with: import csv import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt plt.switch_backend('newbackend') seq_num=[] win=[] def get_data(filename): with open(filename, 'r') as csvfile: csvFileReader = csv.reader(csvfile) next(csvFileReader) # skipping column names for row in csvFileReader: seq_num.append(int(row[0]) win.append(int(row[6])) return def predict_win(X, y, x): win = np

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have successfully trained, exported and uploaded my 'retrained_graph.pb' to ML Engine. My export script is as follows: import tensorflow as tf from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants from tensorflow.python.saved_model import builder as saved_model_builder input_graph = 'retrained_graph.pb' saved_model_dir = 'my_model' with tf.Graph().as_default() as graph: # Read in the export graph with tf.gfile.FastGFile(input_graph, 'rb') as f: graph_def = tf.GraphDef() graph

I used RandomForest for a regression problem. I used importance(rf,type=1) to get the %IncMSE for the variables and one of them has a negative %IncMSE. Does this mean that this variable is bad for the model? I searched the Internet to get some answers but I didn't find a clear one. I also found something strange in the model's summary ( attached below), It seems that only one tree was used although I defined ntrees as 800. model: rf<-randomForest(var1~va2+var3+..+var35,data=d7depo,ntree=800,keep.forest=FALSE, importance=TRUE) summary(rf) Length Class Mode call 6 -none- call type 1 -none-

I am new to scikit-learn library and have been trying to play with it for prediction of stock prices. I was going through its documentation and got stuck at the part where they explain OneHotEncoder() . Here is the code that they have used : >>> from sklearn.preprocessing import OneHotEncoder >>> enc = OneHotEncoder() >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>, handle_unknown='error', n_values='auto', sparse=True) >>> enc.n_values_ array([2, 3, 4]) >>> enc.feature_indices_ array([0, 2, 5, 9]) >>> enc.transform

In computer architecture, what is difference between (branch) prediction and speculation?? These seems very similar, but i think there is a subtle distinction between them. Guffa Branch prediction is done by the processor to try to determine where the execution will continue after a conditional jump, so that it can read the next instruction(s) from memory. Speculative execution goes one step further and determines what the result would be from executing the next instruction(s). If the branch prediction was correct, the result is used, otherwise it is discarded. Note that speculative execution

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I am trying to predict the hygrothermal response of a wall, given the interior and exterior climate. Based on literature research, I believe this should be possible with RNN but I have not been able to get good accuracy. The dataset has 12 input features (time-series of exterior and interior climate data) and 10 output features (time-series of hygrothermal response), both containing hourly values for 10 years. This data was created with hygrothermal simulation software, there is no missing data. Dataset features: Dataset targets: Unlike most time-series prediction problems, I want to predict