valueerror

问题 I am starting with NumPy. Given two np.array s, queu and new_path : queu = [ [[0 0] [0 1]] ] new_path = [ [[0 0] [1 0] [2 0]] ] My goal is to get the following queu : queu = [ [[0 0] [0 1]] [[0 0] [1 0] [2 0]] ] I've tried: np.append(queu, new_path, 0) and np.vstack((queu, new_path)) But both are raising all the input array dimensions except for the concatenation axis must match exactly I didn't get the NumPy philosophy. What am I doing wrong? 回答1: In [741]: queu = np.array([[[0,0],[0,1]]])

问题 I am trying to write a program which calculates formula y=√x*x + 3*x - 500, Interval is [x1;x2] and f.e x1=15, x2=25. I tried to use Exception Handling but it didn't help. And the code I try to use now gives me: ValueError: math domain error import math x1 = int(input("Enter first number:")) x2 = int(input("Enter second number:")) print(" x", " y") for x in range(x1, x2): formula = math.sqrt(x * x + 3 * x - 500) if formula < 0: print("square root cant be negative") print(x, round(formula, 2))

问题 While using an arduino code (also connecting pin 13 to A0 in an arduino uno) to have changing values int PinOutput = 13; int PinInput = A0; int inph; int inpl; void setup() { // put your setup code here, to run once: Serial.begin(9600); pinMode(PinInput, INPUT); pinMode(PinOutput, OUTPUT); } void loop() { // put your main code here, to run repeatedly: inpl = analogRead(PinInput)/4; Serial.println(inpl); analogWrite(PinOutput,255); delay(1000); inph = analogRead(PinInput)/4; Serial.println

问题 When I'm fit my model using logistic regression showing me a value error like ValueError: Penalty term must be positive. C=[1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4] for i in C[-9:]: logisticl2 = LogisticRegression(penalty='l2',C=C) logisticl2.fit(X_train,Y_train) probs = logisticl2.predict_proba(X_test) getting error: ValueError: Penalty term must be positive; got (C=[0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0]) 回答1: Looking more closely, you'll realize that you are

问题 I'm trying to follow the example on this notebook. As suggested in this github thread: I've upped the ulimit to 9999. I've already converted the csv files to hdf5 My code fails when trying to open a single hdf5 file into a dataframe: df = vaex.open('data/chat_history_00.hdf5') Here's the rest of the code: import re import glob import vaex import numpy as np def tryint(s): try: return int(s) except: return s def alphanum_key(s): """ Turn a string into a list of string and number chunks. "z23a"

问题 I am currently designing a NoisyNet in Tensorflow, for which I need to define a custom layer. When copying a model containing that custom layer, python raises the error ValueError: Unknown layer: NoisyLayer . The implementation of the layer is provided here. The goal is to copy one network creating a second instance of it. For that purpose, I use the command net_copy = copy.deepcopy(net_original) , which works as long as I don't include the custom layer referred to above in the model to be

问题 I am currently designing a NoisyNet in Tensorflow, for which I need to define a custom layer. When copying a model containing that custom layer, python raises the error ValueError: Unknown layer: NoisyLayer . The implementation of the layer is provided here. The goal is to copy one network creating a second instance of it. For that purpose, I use the command net_copy = copy.deepcopy(net_original) , which works as long as I don't include the custom layer referred to above in the model to be

问题 I am passing a list from flask function to another function, and getting this value error. My code at sending end: @app.route('/process', methods=['POST']) def process(): name = request.form['name'] comment = request.form['comment'] wickets = request.form['wickets'] ga = request.form['ga'] ppballs = request.form['ppballs'] overs = request.form['overs'] score = [name,comment,wickets,ga,ppballs,overs] results = [] results = eval_score(score) print results Receiver end : def ml_model(data): col

问题 I've tried creating an ML model to make some predictions, but I keep running into a stumbling block. Namely, the code seems to be ignoring the imputation instructions I give it, resulting in the following error: ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). Here's my code: import pandas as pd import numpy as np from sklearn.ensemble import AdaBoostRegressor from category_encoders import CatBoostEncoder from sklearn.compose import make_column_transformer

问题 I've tried creating an ML model to make some predictions, but I keep running into a stumbling block. Namely, the code seems to be ignoring the imputation instructions I give it, resulting in the following error: ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). Here's my code: import pandas as pd import numpy as np from sklearn.ensemble import AdaBoostRegressor from category_encoders import CatBoostEncoder from sklearn.compose import make_column_transformer