Related
I am trying to find the main 200 components of a datasets of 846 images (2048x2048x3 RGB) with sklearn.decomposition.IncrementalPCA.
Data are read by cv2 and reshaped into a 2d np array ([846,2048x2048x3] size, float16)
To ensure a smaller memory cost, I used partial_fit() and divide the original data into smaller chunks (batches) in both partial_fit() and transform() steps.
just like the way in this problem's solution:
Python PCA on Matrix too large to fit into memory
Now my code works well for relative smaller size computations, like computing 20 components for 200 images in the datasets. It outputs right outcomes.
However, the tasks demands me to compute 200 components, which leads to the limit that my batch's size should be larger or at least equal to 200. (according to sklearn's document and the information in the terminal when running the code)
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.IncrementalPCA.html
With such big chunk size,I can finish the IPCA model set, but always face MemoryError when doing partial_fit()
What's more, another problem is:
I need to use inverse_transform later, I am not sure if I can use chunk-style compute in this step or not. (In the code below I did not use it.)
What can I do to avoid this MemoryError? Or should I replace IncrementalPCA with some other method instead ? (these alternatives should have some method like inverse_transform())
The all memory I can access to is 131661572 kB(~127GB)
My code:
from sklearn.decomposition import PCA, IncrementalPCA
import numpy as np
import cv2
import os
folder_path = "./output_img"
input=[]
for i in range(1, 847):
if i%10 == 0: print("loading",i,"th image")
# if i == 60: continue #special case, should be skipped
image_path = folder_path+f"/{i}neutral.jpg"
img = cv2.imread(image_path)
input.append(img.reshape(-1))
print("Loaded all",i,"images")
# change into numpy matrix
all_image = np.stack(input,axis=0)
# trans to 0-1 format float64
all_image = (all_image.astype(np.float16))
### shape: #_of_imag x image_pixel_num (50331648 for img_normals case)
# print(all_image)
# print(all_image.shape)
# PCA, keeps 200 features
COM_NUM=200
pca=IncrementalPCA(n_components = COM_NUM)
print("finished IPCA model set")
saving_path = "./principle847"
element_num = all_image.shape[0] # how many elements(rows) we have in the dataset
chunk_size = 220 # how many elements we feed to IPCA at a time
for i in range(0, element_num//chunk_size):
pca.partial_fit(all_image[i*chunk_size : (i+1)*chunk_size])
print("finished PCA fit:",i*chunk_size,"to",(i+1)*chunk_size)
pca.partial_fit(all_image[(i+1)*chunk_size : element_num]) #tail
print("finished PCA fit:",(i+1)*chunk_size,"to",element_num)
for i in range(0, element_num//chunk_size):
if i==0:
result = pca.transform(all_image[i*chunk_size : (i+1)*chunk_size])
else:
tmp = pca.transform(all_image[i*chunk_size : (i+1)*chunk_size])
result = np.concatenate((result, tmp), axis=0)
print("finished PCA transform:",i*chunk_size,"to",(i+1)*chunk_size)
tmp = pca.transform(all_image[(i+1)*chunk_size : element_num]) #tail
result = np.concatenate((result, tmp), axis=0)
print("finished PCA transform:",(i+1)*chunk_size,"to",element_num)
result = pca.inverse_transform(result)
print("PCA mean:",pca.mean_)
mean_img = pca.mean_
mean_img = mean_img.reshape(2048,2048,3)
mean_img = mean_img.astype(np.uint8)
cv2.imwrite(os.path.join(saving_path,("mean.png")),mean_img)
result=result.reshape(-1,2048,2048,3)
# result shape: #_of_componets * 2048 * 2048 * 3
dst = result
# dst=result/np.linalg.norm(result,axis=(3),keepdims=True)
for j in range(0,COM_NUM):
reconImage = (dst)[j]
# reconImage = reconImage.reshape(4096,4096,3)
reconImage = np.clip(reconImage,0,255)
reconImage = reconImage.astype(np.uint8)
cv2.imwrite(os.path.join(saving_path,("p"+str(j)+".png")),reconImage)
print("Saved",j+1,"principle imgs")
The error goes like:
File "model_generate.py", line 36, in <module>
pca.partial_fit(all_image[i*chunk_size : (i+1)*chunk_size])
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/sklearn/decomposition/_incremental_pca.py", line 299, in partial_fit
U, V = svd_flip(U, V, u_based_decision=False)
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/sklearn/utils/extmath.py", line 538, in svd_flip
max_abs_rows = np.argmax(np.abs(v), axis=1)
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 1103, in argmax
return _wrapfunc(a, 'argmax', axis=axis, out=out)
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 56, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
MemoryError
I am trying to create a batched environment version of an SAC agent example from the Tensorflow Agents library, the original code can be found here. I am also using a custom environment.
I am pursuing a batched environment setup in order to better leverage GPU resources in order to speed up training. My understanding is that by passing batches of trajectories to the GPU, there will be less overhead incurred when passing data from the host (CPU) to the device (GPU).
My custom environment is called SacEnv, and I attempt to create a batched environment like so:
py_envs = [SacEnv() for _ in range(0, batch_size)]
batched_env = batched_py_environment.BatchedPyEnvironment(envs=py_envs)
tf_env = tf_py_environment.TFPyEnvironment(batched_env)
My hope is that this will create a batched environment consisting of a 'batch' of non-batched environments. However I am receiving the following error when running the code:
ValueError: Cannot assign value to variable ' Accumulator:0': Shape mismatch.The variable shape (1,), and the assigned value shape (32,) are incompatible.
with the stack trace:
Traceback (most recent call last):
File "/home/gary/Desktop/code/sac_test/sac_main2.py", line 370, in <module>
app.run(main)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/absl/app.py", line 312, in run
_run_main(main, args)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/absl/app.py", line 258, in _run_main
sys.exit(main(argv))
File "/home/gary/Desktop/code/sac_test/sac_main2.py", line 366, in main
train_eval(FLAGS.root_dir)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/gin/config.py", line 1605, in gin_wrapper
utils.augment_exception_message_and_reraise(e, err_str)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/gin/utils.py", line 41, in augment_exception_message_and_reraise
raise proxy.with_traceback(exception.__traceback__) from None
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/gin/config.py", line 1582, in gin_wrapper
return fn(*new_args, **new_kwargs)
File "/home/gary/Desktop/code/sac_test/sac_main2.py", line 274, in train_eval
results = metric_utils.eager_compute(
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/gin/config.py", line 1605, in gin_wrapper
utils.augment_exception_message_and_reraise(e, err_str)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/gin/utils.py", line 41, in augment_exception_message_and_reraise
raise proxy.with_traceback(exception.__traceback__) from None
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/gin/config.py", line 1582, in gin_wrapper
return fn(*new_args, **new_kwargs)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/eval/metric_utils.py", line 163, in eager_compute
common.function(driver.run)(time_step, policy_state)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/drivers/dynamic_episode_driver.py", line 211, in run
return self._run_fn(
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/utils/common.py", line 188, in with_check_resource_vars
return fn(*fn_args, **fn_kwargs)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/drivers/dynamic_episode_driver.py", line 238, in _run
tf.while_loop(
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/drivers/dynamic_episode_driver.py", line 154, in loop_body
observer_ops = [observer(traj) for observer in self._observers]
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/drivers/dynamic_episode_driver.py", line 154, in <listcomp>
observer_ops = [observer(traj) for observer in self._observers]
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/metrics/tf_metric.py", line 93, in __call__
return self._update_state(*args, **kwargs)
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/metrics/tf_metric.py", line 81, in _update_state
return self.call(*arg, **kwargs)
ValueError: in user code:
File "/home/gary/anaconda3/envs/py39/lib/python3.9/site-packages/tf_agents/metrics/tf_metrics.py", line 176, in call *
self._return_accumulator.assign(
ValueError: Cannot assign value to variable ' Accumulator:0': Shape mismatch.The variable shape (1,), and the assigned value shape (32,) are incompatible.
In call to configurable 'eager_compute' (<function eager_compute at 0x7fa4d6e5e040>)
In call to configurable 'train_eval' (<function train_eval at 0x7fa4c8622dc0>)
I have dug through the tf_metric.py code to try and understand the error, however I have been unsuccessful. A related issue was solved when I added the batch size (32) to the initializer for the AverageReturnMetric instance, and this issue seems related.
The full code is:
# coding=utf-8
# Copyright 2020 The TF-Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python2, python3
r"""Train and Eval SAC.
All hyperparameters come from the SAC paper
https://arxiv.org/pdf/1812.05905.pdf
To run:
```bash
tensorboard --logdir $HOME/tmp/sac/gym/HalfCheetah-v2/ --port 2223 &
python tf_agents/agents/sac/examples/v2/train_eval.py \
--root_dir=$HOME/tmp/sac/gym/HalfCheetah-v2/ \
--alsologtostderr
\```
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sac_env import SacEnv
import os
import time
from absl import app
from absl import flags
from absl import logging
import gin
from six.moves import range
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from tf_agents.agents.ddpg import critic_network
from tf_agents.agents.sac import sac_agent
from tf_agents.agents.sac import tanh_normal_projection_network
from tf_agents.drivers import dynamic_step_driver
#from tf_agents.environments import suite_mujoco
from tf_agents.environments import tf_py_environment
from tf_agents.environments import batched_py_environment
from tf_agents.eval import metric_utils
from tf_agents.metrics import tf_metrics
from tf_agents.networks import actor_distribution_network
from tf_agents.policies import greedy_policy
from tf_agents.policies import random_tf_policy
from tf_agents.replay_buffers import tf_uniform_replay_buffer
from tf_agents.utils import common
from tf_agents.train.utils import strategy_utils
flags.DEFINE_string('root_dir', os.getenv('TEST_UNDECLARED_OUTPUTS_DIR'),
'Root directory for writing logs/summaries/checkpoints.')
flags.DEFINE_multi_string('gin_file', None, 'Path to the trainer config files.')
flags.DEFINE_multi_string('gin_param', None, 'Gin binding to pass through.')
FLAGS = flags.FLAGS
gpus = tf.config.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
print(e)
#gin.configurable
def train_eval(
root_dir,
env_name='SacEnv',
# The SAC paper reported:
# Hopper and Cartpole results up to 1000000 iters,
# Humanoid results up to 10000000 iters,
# Other mujoco tasks up to 3000000 iters.
num_iterations=3000000,
actor_fc_layers=(256, 256),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(256, 256),
# Params for collect
# Follow https://github.com/haarnoja/sac/blob/master/examples/variants.py
# HalfCheetah and Ant take 10000 initial collection steps.
# Other mujoco tasks take 1000.
# Different choices roughly keep the initial episodes about the same.
#initial_collect_steps=10000,
initial_collect_steps=2000,
collect_steps_per_iteration=1,
replay_buffer_capacity=31250, # 1000000 / 32
# Params for target update
target_update_tau=0.005,
target_update_period=1,
# Params for train
train_steps_per_iteration=1,
#batch_size=256,
batch_size=32,
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
alpha_learning_rate=3e-4,
td_errors_loss_fn=tf.math.squared_difference,
gamma=0.99,
reward_scale_factor=0.1,
gradient_clipping=None,
use_tf_functions=True,
# Params for eval
num_eval_episodes=30,
eval_interval=10000,
# Params for summaries and logging
train_checkpoint_interval=50000,
policy_checkpoint_interval=50000,
rb_checkpoint_interval=50000,
log_interval=1000,
summary_interval=1000,
summaries_flush_secs=10,
debug_summaries=False,
summarize_grads_and_vars=False,
eval_metrics_callback=None):
"""A simple train and eval for SAC."""
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
train_summary_writer = tf.compat.v2.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
py_envs = [SacEnv() for _ in range(0, batch_size)]
batched_env = batched_py_environment.BatchedPyEnvironment(envs=py_envs)
tf_env = tf_py_environment.TFPyEnvironment(batched_env)
eval_py_envs = [SacEnv() for _ in range(0, batch_size)]
eval_batched_env = batched_py_environment.BatchedPyEnvironment(envs=eval_py_envs)
eval_tf_env = tf_py_environment.TFPyEnvironment(eval_batched_env)
time_step_spec = tf_env.time_step_spec()
observation_spec = time_step_spec.observation
action_spec = tf_env.action_spec()
strategy = strategy_utils.get_strategy(tpu=False, use_gpu=True)
with strategy.scope():
actor_net = actor_distribution_network.ActorDistributionNetwork(
observation_spec,
action_spec,
fc_layer_params=actor_fc_layers,
continuous_projection_net=tanh_normal_projection_network
.TanhNormalProjectionNetwork)
critic_net = critic_network.CriticNetwork(
(observation_spec, action_spec),
observation_fc_layer_params=critic_obs_fc_layers,
action_fc_layer_params=critic_action_fc_layers,
joint_fc_layer_params=critic_joint_fc_layers,
kernel_initializer='glorot_uniform',
last_kernel_initializer='glorot_uniform')
tf_agent = sac_agent.SacAgent(
time_step_spec,
action_spec,
actor_network=actor_net,
critic_network=critic_net,
actor_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=actor_learning_rate),
critic_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=critic_learning_rate),
alpha_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=alpha_learning_rate),
target_update_tau=target_update_tau,
target_update_period=target_update_period,
td_errors_loss_fn=td_errors_loss_fn,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step)
tf_agent.initialize()
# Make the replay buffer.
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=batch_size,
max_length=replay_buffer_capacity,
device="/device:GPU:0")
replay_observer = [replay_buffer.add_batch]
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(
buffer_size=num_eval_episodes, batch_size=tf_env.batch_size),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes, batch_size=tf_env.batch_size),
]
eval_policy = greedy_policy.GreedyPolicy(tf_agent.policy)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
collect_policy = tf_agent.collect_policy
train_checkpointer = common.Checkpointer(
ckpt_dir=train_dir,
agent=tf_agent,
global_step=global_step,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
policy_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(train_dir, 'policy'),
policy=eval_policy,
global_step=global_step)
rb_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(train_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=replay_buffer)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=replay_observer + train_metrics,
num_steps=initial_collect_steps)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=replay_observer + train_metrics,
num_steps=collect_steps_per_iteration)
if use_tf_functions:
initial_collect_driver.run = common.function(initial_collect_driver.run)
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
if replay_buffer.num_frames() == 0:
# Collect initial replay data.
logging.info(
'Initializing replay buffer by collecting experience for %d steps '
'with a random policy.', initial_collect_steps)
initial_collect_driver.run()
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# Prepare replay buffer as dataset with invalid transitions filtered.
def _filter_invalid_transition(trajectories, unused_arg1):
return ~trajectories.is_boundary()[0]
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=2).unbatch().filter(
_filter_invalid_transition).batch(batch_size).prefetch(5)
# Dataset generates trajectories with shape [Bx2x...]
iterator = iter(dataset)
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
if use_tf_functions:
train_step = common.function(train_step)
global_step_val = global_step.numpy()
while global_step_val < num_iterations:
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
global_step_val = global_step.numpy()
if global_step_val % log_interval == 0:
logging.info('step = %d, loss = %f', global_step_val,
train_loss.loss)
steps_per_sec = (global_step_val - timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
tf.compat.v2.summary.scalar(
name='global_steps_per_sec', data=steps_per_sec, step=global_step)
timed_at_step = global_step_val
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(
train_step=global_step, step_metrics=train_metrics[:2])
if global_step_val % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step_val)
metric_utils.log_metrics(eval_metrics)
if global_step_val % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step_val)
if global_step_val % policy_checkpoint_interval == 0:
policy_checkpointer.save(global_step=global_step_val)
if global_step_val % rb_checkpoint_interval == 0:
rb_checkpointer.save(global_step=global_step_val)
return train_loss
def main(_):
tf.compat.v1.enable_v2_behavior()
logging.set_verbosity(logging.INFO)
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_param)
train_eval(FLAGS.root_dir)
if __name__ == '__main__':
flags.mark_flag_as_required('root_dir')
app.run(main)
What is the appropriate way to create a batched environment for a custom, non-batched environment? I can share my custom environment, but I don't believe the issue lies there as the code works fine when using batch sizes of 1.
Also, any tips on increasing GPU utilization in reinforcement learning scenarios would be greatly appreciated. I have examined examples of using tensorboard-profiler to profile GPU utilization, but it seems these require callbacks and a fit function, which doesn't seem to be applicable in RL use-cases.
It turns out I neglected to pass batch_size when initializing the AverageReturnMetric and AverageEpisodeLengthMetric instances.
Hi I just wanted to use my Module again which I defined in Python.
Here is the Code what I saved in Python.
import numpy as np
matrixSize = 10 Qa= np.random.rand(matrixSize, matrixSize) Q_1 = np.dot(Qa, Qa.transpose()) print(Q_1) np.linalg.cholesky(Q_1) ##check if a matrix is positive definite p_1= np.random.uniform(0, 9, size=10) G_1 = np.diag(-1*np.random.uniform(0, 9, size=10)) h_1 = np.random.uniform(-9, 9, size=10)
np.savez_compressed(r'C:\Users\skqkr\Desktop\Semesterarbeit/Chiwan_Q1', Q=Q_1, p=p_1, G=G_1, h=h_1)
and I want use this Code in Jupyter with Juliaprogramm so I typed like this.
using PyCall
#pyimport numpy
using OSQP
using Compat.SparseArrays
using NPZ
# Define problem data
Matrix10 = npzread("C:/Users/skqkr/Desktop/Semesterarbeit/Chiwan_Q1.npz")
P = sparse(Matrix10['Q'])
q = Matrix10['p']
A = sparse(Matrix10['G']))
u = [Matrix10['h'] ]
# Crate OSQP object
prob = OSQP.Model()
# Setup workspace and change alpha parameter
OSQP.setup!(prob; P=P, q=q, A=A, l=l, u=u, alpha=1)
# Solve problem
results = OSQP.solve!(prob)
But there is an Error
KeyError: key 'Q' not found
So how can I fix it? because it works very well when I use this Module in Pythonprogramm
Thanks!
I am trying to solve the 1D time dependent Schroedinger equation using finite difference methods, here is how the equation looks and how it undergoes discretization
Say I have N spatial points (the x_i goes from 0 to N-1), and suppose my time span is K time points.
I strive to get a K by N matrix. each row (j) will be the function at time t_j
I suspect that my issue is that I am defining the system of the coupled equations in a wrong way.
My boundary conditions are psi=0, or some constant at the sides of the box so I make the ode's in the sides of my X span to be zero
My Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import solve_ivp
#Defining the length and the resolution of our x vector
length = 2*np.pi
delta_x = .01
# create a vector of X values, and the number of X values
def create_x_vector(length, delta_x):
x = np.arange(-length, length, delta_x)
N = len(x)
return x, N
# create initial condition vector
def create_initial_cond(x,x0,Gausswidth):
psi0 = np.exp((-(x-x0)**2)/Gausswidth)
return psi0
#create the system of ODEs
def ode_system(psi,t,delta_x,N):
psi_t = np.zeros(N)
psi_t[0]=0
psi_t[N-1]=0
for i in range(1,N-1):
psi_t[i] = (psi[i+1]-2*psi[i]+psi[i-1])/(delta_x)**2
return psi_t
#Create the actual time, x and initial condition vectors using the functions
t = np.linspace(0,15,5000)
x,N= create_x_vector(length,delta_x)
psi0 = create_initial_cond(x,0,1)
psi = np.zeros(N)
psi= solve_ivp(ode_system(psi,t,delta_x,N),[0,15],psi0,method='Radau',max_step=0.1)
After running I get an error:
runfile('D:/Studies/Project/Simulation Test/Test2.py', wdir='D:/Studies/Project/Simulation Test')
Traceback (most recent call last):
File "<ipython-input-16-bff0a1fd9937>", line 1, in <module>
runfile('D:/Studies/Project/Simulation Test/Test2.py', wdir='D:/Studies/Project/Simulation Test')
File "C:\Users\Pasha\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 704, in runfile
execfile(filename, namespace)
File "C:\Users\Pasha\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "D:/Studies/Project/Simulation Test/Test2.py", line 35, in <module>
psi= solve_ivp(ode_system(psi,t,delta_x,N),[0,15],psi0,method='Radau',max_step=0.1)
File "C:\Users\Pasha\Anaconda3\lib\site-packages\scipy\integrate\_ivp\ivp.py", line 454, in solve_ivp
solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)
File "C:\Users\Pasha\Anaconda3\lib\site-packages\scipy\integrate\_ivp\radau.py", line 288, in __init__
self.f = self.fun(self.t, self.y)
File "C:\Users\Pasha\Anaconda3\lib\site-packages\scipy\integrate\_ivp\base.py", line 139, in fun
return self.fun_single(t, y)
File "C:\Users\Pasha\Anaconda3\lib\site-packages\scipy\integrate\_ivp\base.py", line 21, in fun_wrapped
return np.asarray(fun(t, y), dtype=dtype)
TypeError: 'numpy.ndarray' object is not callable
In a more general note, how can I make python solve N ode's without manually defining each and one of them?
I want to have a big vector called xdot where each cell in this vector will be a function of some X[i]'s and I seem to fail to do that? or maybe my approach is completely wrong?
Also I feel maybe that the "Vectorized" argument of ivp_solve can be connected, but I do not understand the explanation in the SciPy documentation.
The problem is probably that solve_ivp expects a function for its first parameter, and you provided ode_system(psi,t,delta_x,N) which results in a matrix instead (therefore you get type error - ndarray).
You need to provide solve_ivp a function that accepts two variables, t and y (which in your case is psi). It can be done like this:
def temp_function(t, psi):
return ode_system(psi,t,delta_x,N)
and then, your last line should be:
psi= solve_ivp(temp_function,[0,15],psi0,method='Radau',max_step=0.1)
This code solved the problem for me.
For a shorthand way of doing this, you can also just write the function inline using Lambda :
psi= solve_ivp(lambda t,psi : ode_system(psi,t,delta_x,N),[0,15],psi0,method='Radau',max_step=0.1)
Strange error from numpy via matplotlib when trying to get a histogram of a tiny toy dataset. I'm just not sure how to interpret the error, which makes it hard to see what to do next.
Didn't find much related, though this nltk question and this gdsCAD question are superficially similar.
I intend the debugging info at bottom to be more helpful than the driver code, but if I've missed something, please ask. This is reproducible as part of an existing test suite.
if n > 1:
return diff(a[slice1]-a[slice2], n-1, axis=axis)
else:
> return a[slice1]-a[slice2]
E TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U1') dtype('<U1') dtype('<U1')
../py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py:1567: TypeError
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(1567)diff()
-> return a[slice1]-a[slice2]
(Pdb) bt
[...]
py2.7.11-venv/lib/python2.7/site-packages/matplotlib/axes/_axes.py(5678)hist()
-> m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(606)histogram()
-> if (np.diff(bins) < 0).any():
> py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(1567)diff()
-> return a[slice1]-a[slice2]
(Pdb) p numpy.__version__
'1.11.0'
(Pdb) p matplotlib.__version__
'1.4.3'
(Pdb) a
a = [u'A' u'B' u'C' u'D' u'E']
n = 1
axis = -1
(Pdb) p slice1
(slice(1, None, None),)
(Pdb) p slice2
(slice(None, -1, None),)
(Pdb)
I got the same error, but in my case I am subtracting dict.key from dict.value. I have fixed this by subtracting dict.value for corresponding key from other dict.value.
cosine_sim = cosine_similarity(e_b-e_a, w-e_c)
here I got error because e_b, e_a and e_c are embedding vector for word a,b,c respectively. I didn't know that 'w' is string, when I sought out w is string then I fix this by following line:
cosine_sim = cosine_similarity(e_b-e_a, word_to_vec_map[w]-e_c)
Instead of subtracting dict.key, now I have subtracted corresponding value for key
I had a similar issue where an integer in a row of a DataFrame I was iterating over was of type numpy.int64. I got the
TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U1') dtype('<U1') dtype('<U1')
error when trying to subtract a float from it.
The easiest fix for me was to convert the row using pd.to_numeric(row).
Why is it applying diff to an array of strings.
I get an error at the same point, though with a different message
In [23]: a=np.array([u'A' u'B' u'C' u'D' u'E'])
In [24]: np.diff(a)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-24-9d5a62fc3ff0> in <module>()
----> 1 np.diff(a)
C:\Users\paul\AppData\Local\Enthought\Canopy\User\lib\site-packages\numpy\lib\function_base.pyc in diff(a, n, axis)
1112 return diff(a[slice1]-a[slice2], n-1, axis=axis)
1113 else:
-> 1114 return a[slice1]-a[slice2]
1115
1116
TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray'
Is this a array the bins parameter? What does the docs say bins should be?
I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue. I was trying to concatenate rather than take the difference but I think the principle is the same here. I provided a similar answer on another question so I hope that is OK.
In essence you need to use a different data type cast, in my case I needed str not float, I suspect yours is the same so my suggested solution is. I am sorry I cannot test it before suggesting but I am unclear from your example what you were doing.
return diff(str(a[slice1])-str(a[slice2]), n-1, axis=axis)
Please see my example code below for the fix to my code, the change occurs on the third to last line. The code is to produce a basic random forest model.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
This leads to an error of;
Traceback (most recent call last):
File "min_example.py", line 40, in <module>
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
These examples are from a larger code so I hope the examples are clear enough.
I think #James is right. I got stuck by same error while working on Polyval(). And yeah solution is to use the same type of variabes. You can use typecast to cast all variables in the same type.
BELOW IS A EXAMPLE CODE
import numpy
P = numpy.array(input().split(), float)
x = float(input())
print(numpy.polyval(P,x))
here I used float as an output type. so even the user inputs the INT value (whole number). the final answer will be typecasted to float.
I ran into the same issue, but in my case it was just a Python list instead of a Numpy array used. Using two Numpy arrays solved the issue for me.