Experiment 2: Logistic Regression and Newton’s Method August 29, 2018 1 Description In this exercise, you will use Newton’s Method to implement logistic regression on a classification problem. 2 Data into your program and add the x0 = 1 intercept term into your x matrix. Before beginning Newton’s Method, we will first plot the data using different symbols to represent the two classes. In Matlab/Octave it is not a must to perform this transformation, since both gradient ascent algorithm and Newton’s method can be applied to resolve maximization problems. 2 One approach to minimize the above objective

task Whenever we want to execute our task, we use the delay() method of the task class. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Executing to actually send e-mails about. Task.serializer A string identifying the default serialization method to use. Defaults to the CELERY_TASK_SERIALIZER setting. Can be pickle json, yaml, or any custom serialization get_object_or_404(Entry, slug=slug) remote_addr = request.META.get("REMOTE_ADDR") if request.method == "post": form = CommentForm(request.POST, request.FILES) if form.is_valid():

task Whenever we want to execute our task, we use the delay() method of the task class. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Executing exceptions to actually send emails about. Task.serializer A string identifying the default serialization method to use. Defaults to the CELERY_TASK_SERIALIZER setting. Can be pickle json, yaml, or any custom serialization custom task classes All tasks inherit from the celery.task.Task class. The tasks body is its run() method. The following code, @task def add(x, y): return x + y will do roughly this behind the scenes:

task Whenever we want to execute our task, we use the delay() method of the task class. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Executing exceptions to actually send emails about. Task.serializer A string identifying the default serialization method to use. Defaults to the CELERY_TASK_SERIALIZER setting. Can be pickle json, yaml, or any custom serialization custom task classes All tasks inherit from the celery.task.Task class. The task’s body is its run() method. The following code, @task def add(x, y): return x + y will do roughly this behind the scenes:

task Whenever we want to execute our task, we use the delay() method of the task class. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Executing exceptions to actually send emails about. Task.serializer A string identifying the default serialization method to use. Defaults to the CELERY_TASK_SERIALIZER setting. Can be pickle json, yaml, or any custom serialization custom task classes All tasks inherit from the celery.task.Task class. The task’s body is its run() method. The following code, @task def add(x, y): return x + y will do roughly this behind the scenes:

task Whenever we want to execute our task, we use the delay() method of the task class. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Executing exceptions to actually send emails about. Task.serializer A string identifying the default serialization method to use. Defaults to the CELERY_TASK_SERIALIZER setting. Can be pickle json, yaml, or any custom serialization custom task classes All tasks inherit from the celery.task.Task class. The task’s body is its run() method. The following code, @task def add(x, y): return x + y will do roughly this behind the scenes:

task Whenever we want to execute our task, we use the delay() method of the task class. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Executing to actually send e-mails about. Task.serializer A string identifying the default serialization method to use. Defaults to the CELERY_TASK_SERIALIZER setting. Can be pickle json, yaml, or any custom serialization get_object_or_404(Entry, slug=slug) remote_addr = request.META.get("REMOTE_ADDR") if request.method == "post": form = CommentForm(request.POST, request.FILES) if form.is_valid():

celery help Calling the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Calling Tasks): the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready() False You can wait tell your Celery instance to use a configuration module, by calling the app.config_from_object() method: app.config_from_object('celeryconfig') This module is often called “celeryconfig”, but you can

celery help Calling the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Calling Tasks): the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready() False You can wait tell your Celery instance to use a configuration module, by calling the app.config_from_object() method: app.config_from_object('celeryconfig') This module is often called “celeryconfig”, but you can

celery help Calling the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready() False You can wait can tell your Celery instance to use a configuration module by calling the app.config_from_object() method: app.config_from_object('celeryconfig') This module is often called “celeryconfig”, but you can