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ufunc 'add' 不包含带有签名匹配类型的循环 (dtype(' <u32'), dtype('<u32')) -> dtype(' <u32')< div><div id="text_translate"><p> 我正在尝试运行此脚本,但它显示生成的错误:</p><pre> UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32')</pre><p> 这是我试图运行的下面的代码:</p><pre> if __name__ == '__main__': app = Nominatim(user_agent="test_solar") loc_raw = app.geocode('Postintaival 7, 00230 Helsinki, Finland').raw latitude = loc_raw['lat'] longitude = loc_raw['lon'] altitude = get_elevation(latitude, longitude) location_object = Location(latitude, longitude, 'Europe/Helsinki', altitude, 'relex_solutions') weather = pvlib.iotools.get_pvgis_tmy(latitude, longitude, map_variables=True)[0] times = weather.index solpos = location_object.get_solarposition(times) clearsky_values = location_object.get_clearsky(times, model='ineichen', solar_position=solpos, dni_extra=None)</pre> </div></u32')<></u32'),>

[英]ufunc 'add' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32')

I am trying to run this script but it's showing generated error:我正在尝试运行此脚本,但它显示生成的错误:

UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32')

This is the code below I am trying to run:这是我试图运行的下面的代码:

if __name__ == '__main__':
    app = Nominatim(user_agent="test_solar")

    loc_raw = app.geocode('Postintaival 7, 00230 Helsinki, Finland').raw

    latitude = loc_raw['lat']
    longitude = loc_raw['lon']
    altitude = get_elevation(latitude, longitude)

    location_object = Location(latitude, longitude, 'Europe/Helsinki', altitude,
                               'relex_solutions')

    weather = pvlib.iotools.get_pvgis_tmy(latitude, longitude, map_variables=True)[0]

    times = weather.index

    solpos = location_object.get_solarposition(times)

    clearsky_values = location_object.get_clearsky(times, model='ineichen',
                                                   solar_position=solpos,
                                                   dni_extra=None)

                                

TLDR TLDR

Convert your longitude (and also the latitude for consistency) to float and it will work:将您的经度(以及一致性的纬度)转换为浮动,它将起作用:

    latitude = float(loc_raw['lat'])
    longitude = float(loc_raw['lon'])

Longer explanation更长的解释

It takes some times to add all the import and missing function for having a minimum reproducible example.需要一些时间来添加所有导入和缺少的 function 以获得最小的可重现示例。 I guess that the get_elevation function is coming from this question .我猜get_elevation function 来自这个问题

import requests
from geopy.geocoders import Nominatim
from pvlib.location import Location
import pvlib
import pandas as pd


def get_elevation(lat, long):
    query = ('https://api.open-elevation.com/api/v1/lookup'
             f'?locations={lat},{long}')
    r = requests.get(query).json()  # json object, various ways you can extract value
    # one approach is to use pandas json functionality:
    elevation = pd.io.json.json_normalize(r, 'results')['elevation'].values[0]
    return elevation


if __name__ == '__main__':
    app = Nominatim(user_agent="test_solar")

    loc_raw = app.geocode('Postintaival 7, 00230 Helsinki, Finland').raw

    latitude = loc_raw['lat']
    longitude = loc_raw['lon']
    altitude = get_elevation(latitude, longitude)

    location_object = Location(latitude, longitude, 'Europe/Helsinki',
                               altitude, 'relex_solutions')
    weather = pvlib.iotools.get_pvgis_tmy(latitude, longitude,
                                          map_variables=True)[0]

    times = weather.index

    solpos = location_object.get_solarposition(times)
    clearsky_values = location_object.get_clearsky(times, model='ineichen',
                                                   solar_position=solpos,)

Here where your problem is coming in pvlib.spa module:您的问题出在pvlib.spa模块中:

@jcompile('float64(float64, float64, float64)', nopython=True)
def local_hour_angle(apparent_sidereal_time, observer_longitude,
                     sun_right_ascension):
    """Measured westward from south"""
    H = apparent_sidereal_time + observer_longitude - sun_right_ascension
    return H % 360

I added some print in order to see the variables (a debug point should also work):我添加了一些打印以查看变量(调试点也应该有效):

@jcompile('float64(float64, float64, float64)', nopython=True)
def local_hour_angle(apparent_sidereal_time, observer_longitude,
                     sun_right_ascension):
    """Measured westward from south"""
    print(f'{apparent_sidereal_time!r}')
    print(f'{observer_longitude!r}')
    print(f'{sun_right_ascension!r}')
    H = apparent_sidereal_time + observer_longitude - sun_right_ascension
    return H % 360

And we get:我们得到:

array([100.06452967, 115.10559772, 130.14666594, ...,  55.20775781,
        70.24882694,  85.28989625])
'24.9181469'
array([280.83336392, 280.87939039, 280.92541462, ..., 280.99011537,
       281.03612276, 281.0821279 ])

The observer_longitude argument is a string which is not manage in numpy sum operation. observer_longitude参数是一个字符串,在 numpy 求和运算中不受管理。 After some exploration, I find that this longitude is directly coming from the Location object and thus the simple conversion of it into float solves the matter.经过一番探索,我发现这个经度是直接来自Location object 的,因此将它简单地转换为float就解决了这个问题。

UFuncTypeError:ufunc 'clip' 不包含具有签名匹配类型的循环(dtype(' <u32’), dtype(‘<u32’), dtype(‘<u32’)) -> dtype(' <u32’)< div><div id="text_translate"><p> 我使用 Deep Pavlov 框架与 Bert 分类器一起工作,只是因为我需要预测人员的语言是俄语。 基本上,我正在尝试解决多类分类问题。 根据 Deep Pavlov,我们可以轻松地更改配置文件上的一些配置。 我拿了这个配置文件<a href="https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/configs/classifiers/rusentiment_convers_bert.json" rel="nofollow noreferrer">https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/configs/classifiers/rusentiment_convers_bert.json</a>并训练它,结果我花了大约 13 个小时才完成它我的 model 过拟合。</p><p> 我做了一些改变,尤其是这些:</p><pre> "weight_decay_rate": 0.001, "learning_rate_drop_patience": 1, "learning_rate_drop_div": 2.0, "load_before_drop": True, "min_learning_rate": 1e-03, "attention_probs_keep_prob": 0.5, "hidden_keep_prob": 0.5,</pre><p> 另外,我增加了批量大小,之前是 16:</p><pre> "batch_size": 32</pre><p> 并添加了一些指标:</p><pre> "log_loss", "matthews_correlation",</pre><p> 还将validation_patience更改为1并添加了tensorboard func</p><pre> "validation_patience": 1, "tensorboard_log_dir": "logs/",</pre><p> 就是这样。 这些是我对 model 所做的所有更改,当我尝试训练我的 model 时,它给了我以下错误:</p><pre> UFuncTypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds) 60 try: ---&gt; 61 return bound(*args, **kwds) 62 except TypeError: 15 frames UFuncTypeError: ufunc 'clip' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32') During handling of the above exception, another exception occurred: UFuncTypeError Traceback (most recent call last) &lt;__array_function__ internals&gt; in clip(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py in _clip_dep_invoke_with_casting(ufunc, out, casting, *args, **kwargs) 83 # try to deal with broken casting rules 84 try: ---&gt; 85 return ufunc(*args, out=out, **kwargs) 86 except _exceptions._UFuncOutputCastingError as e: 87 # Numpy 1.17.0, 2019-02-24 UFuncTypeError: ufunc 'clip' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32')</pre><p> 起初,我认为它与数据集有关,但是,我没有更改我的数据集,并且在我第一次训练这个 model 时它已经运行。 </p></div></u32’)<></u32’),> - UFuncTypeError: ufunc ‘clip’ did not contain a loop with signature matching types (dtype(‘<U32’), dtype(‘<U32’), dtype(‘<U32’)) -> dtype(‘<U32’)

UFuncTypeError:ufunc 'matmul' 不包含具有签名匹配类型的循环(dtype(' <u32'), dtype('<u32')) -> dtype(' <u32') - streamlit< div><div id="text_translate"><pre> #Linear Regression Model @st.cache(allow_output_mutation=True) def linearRegression(X_train, X_test, y_train, y_test): model = LinearRegression() model.fit(X_train,y_train) score = model.score(X_test, y_test)*100 return score, model</pre><hr><pre> #User input for the model def user_input(): bedrooms = st.slider("Bedrooms: ", 1,15) bathrooms = st.text_input("Bathrooms: ") sqft_living = st.text_input("Square Feet: ") sqft_lot = st.text_input("Lot Size: ") floors = st.text_input("Number Of Floors: ") waterfront = st.text_input("Waterfront? For Yes type '1', For No type '0': ") view = st.slider("View (A higher score will mean a better view): ", 0,4) condition = st.slider("House Condition (A higher score will mean a better condition): ", 1,5) yr_built = st.text_input("Year Built: ") yr_reno = st.text_input("A Renovated Property? For Yes type '1', For No type '0': ") zipcode = st.text_input("Zipcode (5 digit): ") year_sold = st.text_input("Year Sold: ") month_sold = st.slider("Month Sold: ", 1,12) user_input_prediction = np.array([bedrooms,bathrooms,sqft_living, sqft_lot,floors,waterfront,view,condition,yr_built,yr_reno,zipcode,year_sold,month_sold]).reshape(1,-1) return(user_input_prediction)</pre><hr><pre> #Main function if(st.checkbox("Start a Search")): user_input_prediction = user_input() st.write('error1') pred = model.predict(user_input_prediction) st.write('error2') if(st.button("Submit")): st.text("success")</pre><p> 我正在使用 Streamlit 构建一个接受用户输入的 ML model。 在我的主要 function 中,它返回错误UFuncTypeError: ufunc 'matmul' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32') and trace返回pred = model.predict(user_input_prediction)主 function 将打印出 error1 但不会打印 error2</p></div></u32')></u32'),> - UFuncTypeError: ufunc 'matmul' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32') - Streamlit

UFuncTypeError: 无法从 dtype(' <u32') to dtype('float32') with casting rule 'same_kind'?< div><div id="text_translate"><p> 我正在尝试创建一个 ML model 来对石头、纸和剪刀的手势图像进行分类。 我不断收到如下错误消息:</p><blockquote><p> UFuncTypeError:无法使用转换规则“same_kind”将 ufunc 'multiply' output 从 dtype('&lt;U32') 转换为 dtype('float32')</p></blockquote><p> 这是我的代码:</p><pre> import tensorflow as to from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow import keras from tensorflow.keras import layers:wget --no-check-certificate \ https.//dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip -O /tmp/rockpaperscissors,zip import zipfile.os local_zip = '/tmp/rockpaperscissors.zip' zip_ref = zipfile,ZipFile(local_zip. 'r') zip_ref.extractall('/tmp') zip_ref.close(),pip install split_folders import split_folders as SF sf,ratio('/tmp/rockpaperscissors/rps-cv-images', output="/tmp/rockpaperscissors/data".seed=1337, ratio=(.8. .2)) root_path = '/tmp/rockpaperscissors/data' train_path = os,path.join(root_path. 'train') validation_path = os,path,join(root_path, 'val') train_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0,2, fill_mode = 'nearest') test_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0.2, fill_mode = 'nearest') train_generator = train_datagen,flow_from_directory( train_path, target_size=(150, 150). batch_size=32, class_mode='categorical') validation_generator = test_datagen,flow_from_directory( validation_path, target_size=(150, 150). batch_size=32. class_mode='categorical') model = keras.Sequential() model,add(layers,Conv2D(32, (5,5), activation='relu', input_shape=(150. 150. 3))) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(64, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(128, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(256, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(512, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model.add(layers.Flatten()) model,add(layers.Dense(512. activation='relu')) model,add(layers.Dense(3. activation='softmax')) model.summary() loss_fn = keras.losses,SparseCategoricalCrossentropy() model,compile(loss=loss_fn. optimizer=RMSprop(), metrics=['accuracy']) model,fit( train_generator, steps_per_epoch=54, epochs=22, validation_data=validation_generator, validation_steps=13, verbose=2)</pre><p> 这是我的代码的链接: <a href="https://colab.research.google.com/drive/1stBPFyuIQTU_2LqDSHLlrLOSSBeuYLNT#scrollTo=r3Q3w-Tm6tnX" rel="nofollow noreferrer">Rock Paper Scissors Classifier</a>谢谢!</p></div></u32')> - UFuncTypeError: Cannot cast ufunc 'multiply' output from dtype('<U32') to dtype('float32') with casting rule 'same_kind'?

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相关问题 UFuncTypeError:ufunc 'clip' 不包含具有签名匹配类型的循环(dtype(' <u32’), dtype(‘<u32’), dtype(‘<u32’)) -> dtype(' <u32’)< div><div id="text_translate"><p> 我使用 Deep Pavlov 框架与 Bert 分类器一起工作,只是因为我需要预测人员的语言是俄语。 基本上,我正在尝试解决多类分类问题。 根据 Deep Pavlov,我们可以轻松地更改配置文件上的一些配置。 我拿了这个配置文件<a href="https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/configs/classifiers/rusentiment_convers_bert.json" rel="nofollow noreferrer">https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/configs/classifiers/rusentiment_convers_bert.json</a>并训练它,结果我花了大约 13 个小时才完成它我的 model 过拟合。</p><p> 我做了一些改变,尤其是这些:</p><pre> "weight_decay_rate": 0.001, "learning_rate_drop_patience": 1, "learning_rate_drop_div": 2.0, "load_before_drop": True, "min_learning_rate": 1e-03, "attention_probs_keep_prob": 0.5, "hidden_keep_prob": 0.5,</pre><p> 另外,我增加了批量大小,之前是 16:</p><pre> "batch_size": 32</pre><p> 并添加了一些指标:</p><pre> "log_loss", "matthews_correlation",</pre><p> 还将validation_patience更改为1并添加了tensorboard func</p><pre> "validation_patience": 1, "tensorboard_log_dir": "logs/",</pre><p> 就是这样。 这些是我对 model 所做的所有更改,当我尝试训练我的 model 时,它给了我以下错误:</p><pre> UFuncTypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds) 60 try: ---&gt; 61 return bound(*args, **kwds) 62 except TypeError: 15 frames UFuncTypeError: ufunc 'clip' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32') During handling of the above exception, another exception occurred: UFuncTypeError Traceback (most recent call last) &lt;__array_function__ internals&gt; in clip(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py in _clip_dep_invoke_with_casting(ufunc, out, casting, *args, **kwargs) 83 # try to deal with broken casting rules 84 try: ---&gt; 85 return ufunc(*args, out=out, **kwargs) 86 except _exceptions._UFuncOutputCastingError as e: 87 # Numpy 1.17.0, 2019-02-24 UFuncTypeError: ufunc 'clip' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32')</pre><p> 起初,我认为它与数据集有关,但是,我没有更改我的数据集,并且在我第一次训练这个 model 时它已经运行。 </p></div></u32’)<></u32’),> - UFuncTypeError: ufunc ‘clip’ did not contain a loop with signature matching types (dtype(‘<U32’), dtype(‘<U32’), dtype(‘<U32’)) -> dtype(‘<U32’) TypeError:ufunc&#39;add&#39;不包含签名匹配类型为dtype(&#39;的循环 - TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32') Scikit-Learn(类型错误:ufunc &#39;subtract&#39; 不包含签名匹配类型 dtype(&#39; - Scikit-Learn (TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32')) UFuncTypeError:ufunc 'matmul' 不包含具有签名匹配类型的循环(dtype(' <u32'), dtype('<u32')) -> dtype(' <u32') - streamlit< div><div id="text_translate"><pre> #Linear Regression Model @st.cache(allow_output_mutation=True) def linearRegression(X_train, X_test, y_train, y_test): model = LinearRegression() model.fit(X_train,y_train) score = model.score(X_test, y_test)*100 return score, model</pre><hr><pre> #User input for the model def user_input(): bedrooms = st.slider("Bedrooms: ", 1,15) bathrooms = st.text_input("Bathrooms: ") sqft_living = st.text_input("Square Feet: ") sqft_lot = st.text_input("Lot Size: ") floors = st.text_input("Number Of Floors: ") waterfront = st.text_input("Waterfront? 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Python:Numpy dtype U32 - 简单的 if-else 语句 - Python: Numpy dtype U32 - simple if-else statement UFuncTypeError: 无法从 dtype(' <u32') to dtype('float32') with casting rule 'same_kind'?< div><div id="text_translate"><p> 我正在尝试创建一个 ML model 来对石头、纸和剪刀的手势图像进行分类。 我不断收到如下错误消息:</p><blockquote><p> UFuncTypeError:无法使用转换规则“same_kind”将 ufunc 'multiply' output 从 dtype('&lt;U32') 转换为 dtype('float32')</p></blockquote><p> 这是我的代码:</p><pre> import tensorflow as to from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow import keras from tensorflow.keras import layers:wget --no-check-certificate \ https.//dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip -O /tmp/rockpaperscissors,zip import zipfile.os local_zip = '/tmp/rockpaperscissors.zip' zip_ref = zipfile,ZipFile(local_zip. 'r') zip_ref.extractall('/tmp') zip_ref.close(),pip install split_folders import split_folders as SF sf,ratio('/tmp/rockpaperscissors/rps-cv-images', output="/tmp/rockpaperscissors/data".seed=1337, ratio=(.8. .2)) root_path = '/tmp/rockpaperscissors/data' train_path = os,path.join(root_path. 'train') validation_path = os,path,join(root_path, 'val') train_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0,2, fill_mode = 'nearest') test_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0.2, fill_mode = 'nearest') train_generator = train_datagen,flow_from_directory( train_path, target_size=(150, 150). batch_size=32, class_mode='categorical') validation_generator = test_datagen,flow_from_directory( validation_path, target_size=(150, 150). batch_size=32. class_mode='categorical') model = keras.Sequential() model,add(layers,Conv2D(32, (5,5), activation='relu', input_shape=(150. 150. 3))) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(64, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(128, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(256, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(512, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model.add(layers.Flatten()) model,add(layers.Dense(512. activation='relu')) model,add(layers.Dense(3. activation='softmax')) model.summary() loss_fn = keras.losses,SparseCategoricalCrossentropy() model,compile(loss=loss_fn. optimizer=RMSprop(), metrics=['accuracy']) model,fit( train_generator, steps_per_epoch=54, epochs=22, validation_data=validation_generator, validation_steps=13, verbose=2)</pre><p> 这是我的代码的链接: <a href="https://colab.research.google.com/drive/1stBPFyuIQTU_2LqDSHLlrLOSSBeuYLNT#scrollTo=r3Q3w-Tm6tnX" rel="nofollow noreferrer">Rock Paper Scissors Classifier</a>谢谢!</p></div></u32')> - UFuncTypeError: Cannot cast ufunc 'multiply' output from dtype('<U32') to dtype('float32') with casting rule 'same_kind'? ufunc&#39;add&#39;不包含签名匹配类型为dtype(&#39; - ufunc 'add' did not contain a loop with signature matching types dtype('<U23') dtype('<U23') dtype('<U23')
 
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