I was chatting with my boyfriend about that time Leicester won the premier league. Apparently some betting companies had given odds of 1/5000, which to me seemed intuitively about right.
But how would we go about doing this rigorously?
Laplace has an estimate for how we should calculate the expected value of a binomial random variable of unknown probability, which works like this:
We assign it 1 “heads” and 1 “tails”, then record the actual observed values.
So a coin which has actually landed HTH would be estimated to have a 60% chance of landing H.
Obviously we can’t start by assigning every team 1W and 1L since then the probability that each team wins is 50% which doesn’t make sense, so maybe we should instead extend Laplace’s idea and assign each team 1W and 19L so they each start with a 5% chance of winning, and then continue to proceed with their actually observed wins.
But this also doesn’t feel like it does so well because if Leicester had (hypothetically) won the premier league for the first 10 years of its existence then the probability that they win the 11th year feels like it should be a lot more than 11/30.
Also it doesn’t seem like a team whose record is WWLLLLLLLL is as likely to win as a team whose record is LLLLLLLLWW: the team that won it more in recent years is clearly more likely to win this year, since they still have most of the same players who won it last year.
So instead I considered using a Bayesian approach. Perhaps each team is given a Bayesian prior of 5% in the first season and then we update the priors according to how well each team does each season.
Probability(hypothesis evidence) = probability(evidence hypothesis) probability(hypothesis) / probability (evidence)
So our evidence at each iteration of our Bayesian model is the position the team finished this season. It seems like we need some way to update our priors such that the higher the team finished in the league, the higher the prior that they will win next season.
I’m trying to come up with some way to update the priors according to where each team finished in the league. Obviously finishing higher should increase the priors and finishing lower should decrease them, and the sum of all the next generation of probabilities still has to add to 1.
P(E) and P(EH) don’t seem to have obvious values here which I think is what I’m struggling with. How might I approach this?

Right now I have 18 contractors getting weekly paychecks, their pay is commission based.
I have a small sheet I have made to help calculate their pay a little quicker than doing it fully manual but, I have a feeling I can make it even better and further automize the process.
Right now I have 7 columns and 18 lines, the columns are; A (Contractor ID) B (Contractor Name) C (Quantity of Sales) D (Gross Sales) E (Tips) F (Commission based off of gross) G (Net Earnings)
I get the gross sales and all information needed to calculate their pay emailed to me weekly with each contractor all on one PDF. Each contractor has a different commission rate and I have each line specified to the individual in that specific line, for example: Amanda would make 50% commission so I would have Amandas line look something like this, assuming she is line 2. A (Example#) B (Amanda Example) C (20) D ($1,000.00) E ($100.00) F (=D2*0.50) G (=E2+F2)
As of right now I read the sales report I get emailed to me and manually input sales for each contractor and basically want to know if there is a way that I could take the PDF emailed to me, convert into CSV or something and instantly copy paste just the useful information I would need, column by column, and have those customized commission formulas be synchronized to the individuals name rather than the line because sometimes not all of my contractors worked each week so if I was to just copy paste the columns, one absent contractor would cause every other one beneath to be miss-placed.
To try and simplify the question... Can I have google sheets recognize a name such as, "Amanda Example" and have it know that on column F it needs to be =D?*0.50 regardless of the line that this particular contractor may end up on?

Hi all!
So I have been trying to make a console-based 3D spinning cube program. the code that i have managed to write is as follows:

import math 

import time import os

Variables

cubeWidth = 20 width = 80 height = 44 zBuffer = [0] * (width * height) buffer = [' '] * (width * height) # Initialize buffer with spaces distance_from_cam = 100 K1 = 40.0

Set a value for this in the main program

horizontalOffset = 0
increment_speed = 1
def CalculateX(i, j, k, A, B, C): return j * math.sin(A) * math.sin(B) * math.cos(C) - k * math.cos(A) * math.sin(B) * math.cos(C) + j * math.cos(A) * math.sin(C) + k * math.sin(A) * math.sin(C) + i * math.cos(B) * math.cos(C)
def CalculateY(i, j, k, A, B, C): return j * math.cos(A) * math.cos(C) + k * math.sin(A) * math.cos(C) - j * math.sin(A) * math.sin(B) * math.sin(C) + k * math.cos(A) * math.sin(B) * math.sin(C) - i * math.cos(B) * math.sin(C)
def CalculateZ(i, j, k, A, B): return k * math.cos(A) * math.cos(B) - j * math.sin(A) * math.cos(B) + i * math.sin(B)
def calculateForSurface(cubeX, cubeY, cubeZ, ch, A, B, C): x = CalculateX(cubeX, cubeY, cubeZ, A, B, C) y = CalculateY(cubeX, cubeY, cubeZ, A, B, C) z = CalculateZ(cubeX, cubeY, cubeZ, A, B) + distance_from_cam

# Calculate perspective projection ooz = 1 / z xp = int((width / 2) + horizontalOffset + K1 * ooz * x * 2) yp = int((height / 2) + K1 * ooz * y) idx = xp + yp * width if 0 <= idx < width * height: if ooz > zBuffer[idx]: zBuffer[idx] = ooz buffer[idx] = ch 

Function to clear the screen

def clear_screen(): os.system('cls' if os.name == 'nt' else 'clear') print('\033[H\033[J', end='') # ANSI escape codes to clear the screen and move cursor to top left
start_time = time.time()
while True: elapsed_time = time.time() - start_time

# Clear the screen clear_screen() # Reset zBuffer for each frame zBuffer = [0] * (width * height) # Reset horizontal offset horizontalOffset = 0 # Calculate rotation angles based on elapsed time rotation_speed_x = 1 rotation_speed_y = 2 rotation_speed_z = 0.5 A = rotation_speed_x * elapsed_time B = rotation_speed_y * elapsed_time C = rotation_speed_z * elapsed_time # Rendering code cubeWidth = 20 # Render the cubes for cubeX in range(-cubeWidth, cubeWidth, increment_speed): for cubeY in range(-cubeWidth, cubeWidth, increment_speed): calculateForSurface(cubeX, cubeY, -cubeWidth, '@', A, B, C) calculateForSurface(cubeWidth, cubeY, cubeX, '$', A, B, C) calculateForSurface(-cubeWidth, cubeY, -cubeX, '~', A, B, C) calculateForSurface(-cubeX, cubeY, cubeWidth, '#', A, B, C) calculateForSurface(cubeX, -cubeWidth, -cubeY, ';', A, B, C) calculateForSurface(cubeX, cubeWidth, cubeY, '+', A, B, C) # Print the buffer os.system('cls' if os.name == 'nt' else 'clear') for k in range(0, width * height, 1): print(buffer[k] if k % width != 0 else '\n', end='') clear_screen() time.sleep(1) # Adjust the sleep time for frame rate 

However I have the following problems with this code:
1: the output is not a cube(?????). its as the surface of the cube that is behind is not rotating and is hence giving the output a sort of diamond shape. idk

1. The second is that the output is on different lines, and not clearing the entire terminal screen properly.

​
idk what is happening, I feel so dumb, this coding thing is just not for me. Please help!

​
So, I decided to download the latest patch. They messed up again somewhere, but it's supposed to be a new update for the RU-BDO server. **New update, new update!** All my PvE dreams will come true with this new patch. 700 GS per month, you know? New update, fishing and horse stuff. New update, minus 10k karma. Once again, the update simplifies the game by randomly assigning heroes, but you won't have balance. The trash class will add damage to mana sticks and water fish, and it's sad that there will be lag in the game. Another update, server patch, no more invulnerability. FPS won't work, and I died in the clouds again. The update affects heroes and sieges. The review took 5 hours. The clan arena is trash and the hero editor is one, the variables are removed, and there are long sieges again! You can go free to test the combo, but soon you'll be reading thousands of words again for a few hours.Because the patch is new, rebalancing is happening. Two years later, a new solar patch. A new leader is leaving, and HI-VAR is fading away. There are almost no communities left. You go to your favorite spot, and there's some crazy guy kicking mobs. He's already on the forum, chin-choking, "May there be salvation for you!" You catch a horse, but it's bullshit for beginners. This is for beginners, and a free T9 horse. It's the fifth item to choose from and it doesn't feel like zero anymore. You come to finish it, "I'm a beginner!!! How is that possible?" A new patch restores the stones for donations. A guarantor for sharpening, 50 drops every month. Not a shit eater, but a mixer! You take part in a siege and there are 12 piles nearby. We survived as best as we could., but for some reason, the servers just crashed. You know, they were cutting up some promo codes, and we figured out how to balance the server for you in half a year! New patch, new PVP, new patch, new barcode fired in the basement, new patch from the developers, who ignored everyone again, new patch from PEARL ABYSS, and another new patch from PEARL ABYSS.

TLDR at the bottom
My Full Tensorflow Code: Link. Please excuse all the different commented out parts of code, I've had a long road of trouble shooting this code.
Hardware and Software Setup
-Virtual Machine on Runpod
-NVIDIA A100 GPU
-Tensorflow 2.15
-CUDA 12.2
-cuDNN 8.9
What I'm doing and the issue I'm facing
I am trying to creating a visual generator AI, and to that end I am trying to implement the TGANv2 architecture in Tensorflow. The TGANv2 model I am following was originally written in Chainer by some researchers. I also implemented it in Pytorch (here is my PyTorch code if you are interested) and also ran it in Chainer. It works fine in both. But when I try to implement it in Tensorflow I start running into this error:

Traceback (most recent call last): File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/ops/script_ops.py", line 270, in __call__ ret = func(*args) ^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/data/ops/from_generator_op.py", line 198, in generator_py_func values = next(generator_state.get_iterator(iterator_id)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/3TF-TGANv2.py", line 140, in __iter__ yield self[idx] ~~~~^^^^^ File "/workspace/3TF-TGANv2.py", line 126, in __getitem__ x2 = self.sub_sample(x1) ^^^^^^^^^^^^^^^^^^^ File "/workspace/3TF-TGANv2.py", line 99, in sub_sample x = tf.strided_slice(x, begin, end, strides) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler raise e.with_traceback(filtered_tb) from None File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/eageexecute.py", line 59, in quick_execute except TypeError as e: tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin and size arguments to be 1-D tensors of size 2, but got shapes [4] and [2] instead. [Op:StridedSlice] 

What's important to note about this issue is that it does not come up right away. It can go through dozens of batches before this issue pops up. This error was generated with a batch size of 16, but if I lower my batch size to 8 I can even get it to run for 5 epochs (longest I've tried). The outputs of the Generator are not what I saw with Chainer or Pytorch after 5 epochs (it's mostly just videos of a giant black blob), though I am unsure if this is related to the issue. So with a batch size of 8 sometimes the issue comes up and sometimes it doesn't. If I lower the batch size to 4, the issue almost never comes up. The fact that this is batch size driven really perplexes me. I've tried it with multiple different GPUs.
Description of relevant parts of model and code
The way the Generator works is as follows. There is a CLSTM layer that generates 16 features maps that have a 4x4 resolution and 1024 channels each. Each feature map corresponds to a frame of the output video (the output video has 16 frames and runs at 8fps, so it's a 2 second long gif).
During inference each feature map passes through 6 upsampling blocks, with each upsampling block doubling the resolution and halving the channels. So after 6 blocks the shape of each frame is (256, 256, 16), so it has a 256p resolution and 16 channels. Each frame then gets rendered by a rendering block to render it into a 3-channel image, of shape (256, 256, 3). So the final shape of the output video is (16, 256, 256, 3) = (T, H, W, C), where T is the number of frame, H is the height, W the width, and C the number of channels. This output is a single tensor.
During training the setup is a bit different. The generated output video will be split up into 4 "sub-videos", each of varying resolution and frames. This will output a tuple of tensors: (tensor1, tensor2, tensor3, tensor4). The shapes of each tensor (after going through a rendering block to reduce the channel length to 3)) is tensor1=(16, 32, 32, 3), tensor2=(8, 64, 64, 3), tensor3=(4, 128, 128, 3), tensor4=(2, 256, 256, 3). As you can see, as you go from tensor1 to tensor4 the frame number gets halved each time while the resolution doubles. The real video examples also get split up into 4 sub-video tensors of the same shape. These sub-videos are what are fed into the discriminator. Now the functionality that halves the frame length is called sub-sampling. How the function works is that it starts at either the first or second frame (this is supposed to be random) and then selects every other frame. There is a sub-sample function in both the Videodataset class (which takes the real videos and generates 4 sub-video tensors) and in the Generator class. The Videodataset class outputs 4-D tensors (T, H, W, C), while the Generator class outputs 5 because it has a batch dimension N.
This is the sub-sample function in the VideoDataset class:

 def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 begin = [offset, 0, 0, 0] # start from index 'offset' in the frame dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3]] strides = [frame, 1, 1, 1] # step 'frame' in the Frame dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[0]) // frame #print(f"VD Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[0]}") if x.shape[0] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[0]}") return x 

This is the sub-sample function in the Generator class:

 def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 # begin = [0, offset, 0, 0, 0] # start from index 'offset' in the second dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3], original_shape[4]] strides = [1, frame, 1, 1, 1] # step 'frame' in the second dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[1]) // frame #print(f"Gen Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[1]}") if x.shape[1] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[1]}") return x 

You'll notice I am using tf.strided_slice(). I originally tried slicing/sub-sampling using the same notation you would do for slicing a numpy array: x = x[:,offset::frame,:,:,:]. I changed it because I thought maybe that was causing some sort of issue.
Below is a block diagram of the Generator and VideoDataset (labeled "Dataset" in the block diagram) functionalities.
https://preview.redd.it/2vh7yx2g09xc1.png?width=1862&format=png&auto=webp&s=143d5c4c8df91fc71b9da1d3858feaae28c4605a
A point of note about the block diagram, the outputs of Dataset are NOT combined with the outputs of the Generator, as might be mistakenly deduced based on the drawing. The discriminator outputs predictions on the Generator outputs and the Dataset outputs separately.
I don't think this issue is happening in the backward pass because I put in a bunch of print statements and based on those print statements the error does not occur in the middle of a gradient calculation or backward pass.
My Dataloader and VideoDataset class
Below is how I am actually fetching data from my VideoDataset class:

 #Create dataloader dataset = VideoDataset(directory) dataloader = tf.data.Dataset.from_generator( lambda: iter(dataset), # Corrected to use iter() to clearly return an iterator from the dataset output_signature=( tf.TensorSpec(shape=(16, 32, 32, 3), dtype=tf.float32), tf.TensorSpec(shape=(8, 64, 64, 3), dtype=tf.float32), tf.TensorSpec(shape=(4, 128, 128, 3), dtype=tf.float32), tf.TensorSpec(shape=(2, 256, 256, 3), dtype=tf.float32) ) ).batch(batch_size) 

and here is my VideoDataset class:

class VideoDataset(): def __init__(self, directory, fraction=0.2, sub_sample_rate=2): print("Initializing VD") = directory self.fraction = fraction self.sub_sample_rate = sub_sample_rate all_files = [os.path.join(self.directory, file) for file in os.listdir(self.directory)] valid_files = [] for file in all_files: try: # Read the serialized tensor from file serialized_tensor = tf.io.read_file(file) # Deserialize the tensor tensor = tf.io.parse_tensor(serialized_tensor, out_type=tf.float32) # Adjust dtype if necessary # Validate the shape of the tensor if tensor.shape == (16, 256, 256, 3): valid_files.append(file) except Exception as e: print(f"Error loading file {file}: {e}") # Randomly select a fraction of the valid files selected_file_count = int(len(valid_files) * fraction) print(f"Selected {selected_file_count} files") self.files = random.sample(valid_files, selected_file_count) def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 begin = [offset, 0, 0, 0] # start from index 'offset' in the frame dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3]] strides = [frame, 1, 1, 1] # step 'frame' in the Frame dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[0]) // frame #print(f"VD Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[0]}") if x.shape[0] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[0]}") return x def pooling(self, x, ksize): if ksize == 1: return x T, H, W, C = x.shape Hd = H // ksize Wd = W // ksize # Reshape the tensor to merge the spatial dimensions into the pooling blocks x_reshaped = tf.reshape(x, (T, Hd, ksize, Wd, ksize, C)) # Take the mean across the dimensions 3 and 5, which are the spatial dimensions within each block pooled_x = tf.reduce_mean(x_reshaped, axis=[2, 4]) return pooled_x def __len__(self): return len(self.files) def __getitem__(self, idx): #print("Calling VD getitem method") serialized_tensor = tf.io.read_file(self.files[idx]) video_tensor = tf.io.parse_tensor(serialized_tensor, out_type=tf.float32) x1 = video_tensor x2 = self.sub_sample(x1) x3 = self.sub_sample(x2) x4 = self.sub_sample(x3) #print("\n") x1 = self.pooling(x1, 8) x2 = self.pooling(x2, 4) x3 = self.pooling(x3, 2) #print(f"Shapes of VD output = {x1.shape}, {x2.shape}, {x3.shape}, {x4.shape}") return (x1, x2, x3, x4) def __iter__(self): print(f"Calling VD iter method, len self = {len(self)}") #Make the dataset iterable, allowing it to be used directly with tf.data.Dataset.from_generator. for idx in range(len(self)): yield self[idx]self.directory 

The issue is happening at one point when the dataloader is fetching examples from Videodataset in my opinion, I just can't figure out what is causing it.
TLDR
I am using a runpod VM with an NVIDIA A100 GPU. I am trying to train a GAN that outputs 2 second long gifs that are made up fo 16 frames. One of the training step involves splitting the output video (either real or fake) into 4 sub videos of different frame length and resolution. The reduction of frames is achieve by a sub-sample function (which you can find earlier in my post, it is bolded) that starts at the first or second frame of the video (random) and then selects every other frame, so it halves the frames. So I am essentially doing a strided slice on a tensor, and I am using tf.strided_slice(). I tried using regular slicing notation (like you would use in NumPy), and I get the same error. The weird thing about this is that the issue does NOT come up immediately in training and is dependent on batch size. The training goes through several batch iterations just fine (and sometimes some epochs) with a batch size of 16. If I lower the batch size to 8 it's absle to go thorugh even more iterations, even up to 5 epochs (I didn't test it for longer), although the outputs are not the outputs I would expect after some epochs (I expect a specific type of noisy image based on how this model ran in PyTorch and Chainer frameworks, but I instead get a video that's mostly just a black blob through most of the resolution, just a bit of color on the edges). If I go down to a batch size of 4 the issue goes away mostly. See below for the error I am seeing:
Error:
Expected begin and size arguments to be 1-D tensors of size 2, but got shapes [4] and [2] instead. [Op:StridedSlice]

Hi everyone,
I am unsure how and if I can add images - so sry if there are none to better understand what I'm talking about. I am happy to share the images of the map and stat block in some way if I'm told how :D

TL;DR

I find it really hard to create balanced combat encounters, especially those that are challenging but doable and in the end rewarding (like with boss fights). Therefore I need help adjusting the difficulty of my final boss and the fight for a rather new group of Level 2 players. I used a self-made homebrew version of a GEM STALKER with some adjusted Stats and Lair Actions (s. below). Do you think that is balanced, also given the terrain? Or would you change something to make it fairer?

PARTY

I am currently planning out the final battle for a Homebrew One Shot Adventure for 1st time players and need help with balancing the final boss for my party (haven't done a lot of homebrew monsters so far). It will be their second session but they're doing very well, in terms of understanding the rules, using their abilities and with roleplaying (even in combat).
We're using some simplified rules, the amazing DnD Story Mode made by u/joelesko , which I adjusted myself in terms of adding some more races, an additional feature specific to a kind of subclass and making the druid and ranger class a bit more unique, nothing too major though, that should impact the balance of the rule set too much (at least I hope so xD so far it felt quite balanced). Here's the Class Reference Sheets and Character Creation Guide I made for my players if anyone wants to have a look and have a reference how the characters were build. (Unfortunately my guides are all in German as my players don't speak English very well but maybe you can still gleam the most important information from it as many features are kept in English (for simplicity of referencing) - sry about that)
The party consists of 5 players, each Level 2:

• Tiefling Dual Swords Ranger
• Gnome Illusionist Wizard
• Dwarf Paladin of Courage
• Tiefling Assassin Rogue
• Human Brawler Fighter

I was thinking of maybe leveling them up to Level 3 before the boss fight to make them a bit stronger and more resistant, but am unsure if that's the way to go as they already leveled up once during the last session (after their first fight and completing the first part of the adventure) and I don't want to overwhelm them with options. Or should I just give them like a boost in abilities, I'd have a way to make that plausible in game (same for the level up)

MONSTER: GEM STALKER

Generally they will be fighting a cursed amethyst dragon (called Belayana) with a stat block based on that of an Amethyst Gem Stalker, that is trying to protect a magic tree that gives the surrounding forest and the beings within life energy. They can either kill it (and let the forest die but get the "Quest" money), knock it unconscious and help Belayana to get back to her true Dragon form, by uniting her body with that of the gem tree, or (with a lot of clever thinking and luck) might be able to persuade it to stand down and find a different way.
Belayana will be rather aggressive when fighting as she sees everyone in the cave as a danger to the tree and the forest, and is intend on protecting it no matter what. She will use walls and water and her teleport to her advantage to attack and move around. Although she is turned into a monster, she is still intelligent and acts like it but will fight to the death.
I adjusted the Stat Block of the Gem Stalker with the 5etools integrated CR adjustments in terms of damage and abilities to a CR 2. Below is everything I changed or added (indicated by a +)

• AC: 15
• HP: 45 (7d10 +7)
• To Hit: +3 (STR)
• Speed: + 40ft swim
• Features: + Amphibious
• Multiattack: 2 Claw attacks or 1 Tail Swipe
• Claw: 1d6 +1 dmg
• + Tail Swipe: 10ft range, 1d8 + 1
• Amethyst Crystal Dart: 2d4+1 and Teleport
• Lair Actions: Beguiling Whisper: DC 12 WIS save or be charmed by Monster until next lair action Blinding Pulse: DC 12 CON save or be blinded until next lair action Rising Amethysts: 1 Minion (either Mephit or Earth Elemental) arises in a 30ft range; only 1 minion at a time possible
• Removed: Protective Link

I was thinking of either modifying a mephit (which one though?) to a gem flavour to use as a minion AND/OR use the Small Earth Elemental (adjusted to only one attack per turn) created by u/Kankerata. I usually like using minions, because, imo, then the fight gains a better action economy instead of just circling and pummeling the big bad.
MAP
For the sake of completion and better calculate what difficulty is appropriate:
I used this map by u/FantastiskDoD as a general base and adjusted it to my amethyst setting for Foundry VTT. The map is ~190ft in length and 160ft top to bottom (5ft grid). There are two bodies of water (left and right) and some of the crystals on the ground can be used to hide behind. The big gemstones inside the right lake is a stand-in for the big magical gem tree, that fuels the magic of the forest and of Belayana. There are Stone slips inside the left lake that can be traversed by jumping and if necessary an Athletics Check, but are considered difficult terrain as they are wet and slippery. A hidden entrance through a waterfall is the primary way into the cave. The ground inbetween the lakes is about 30ft wide at its smalles part.
​
Thanks in advance for any help and ideas :D I am happy to answer any questions that might arise or post this somewhere else, where it is more fitting - I am aware it's a lot of information and not the best presented. I just don't know what would be important in that regard >.<

Traceback (most recent call last): File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/ops/script_ops.py", line 270, in __call__ ret = func(*args) ^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/data/ops/from_generator_op.py", line 198, in generator_py_func values = next(generator_state.get_iterator(iterator_id)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/3TF-TGANv2.py", line 140, in __iter__ yield self[idx] ~~~~^^^^^ File "/workspace/3TF-TGANv2.py", line 126, in __getitem__ x2 = self.sub_sample(x1) ^^^^^^^^^^^^^^^^^^^ File "/workspace/3TF-TGANv2.py", line 99, in sub_sample x = tf.strided_slice(x, begin, end, strides) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler raise e.with_traceback(filtered_tb) from None File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/eageexecute.py", line 59, in quick_execute except TypeError as e: tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin and size arguments to be 1-D tensors of size 2, but got shapes [4] and [2] instead. [Op:StridedSlice] 

 def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 begin = [offset, 0, 0, 0] # start from index 'offset' in the frame dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3]] strides = [frame, 1, 1, 1] # step 'frame' in the Frame dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[0]) // frame #print(f"VD Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[0]}") if x.shape[0] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[0]}") return x 

 def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 # begin = [0, offset, 0, 0, 0] # start from index 'offset' in the second dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3], original_shape[4]] strides = [1, frame, 1, 1, 1] # step 'frame' in the second dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[1]) // frame #print(f"Gen Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[1]}") if x.shape[1] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[1]}") return x 

 #Create dataloader dataset = VideoDataset(directory) dataloader = tf.data.Dataset.from_generator( lambda: iter(dataset), # Corrected to use iter() to clearly return an iterator from the dataset output_signature=( tf.TensorSpec(shape=(16, 32, 32, 3), dtype=tf.float32), tf.TensorSpec(shape=(8, 64, 64, 3), dtype=tf.float32), tf.TensorSpec(shape=(4, 128, 128, 3), dtype=tf.float32), tf.TensorSpec(shape=(2, 256, 256, 3), dtype=tf.float32) ) ).batch(batch_size) 

class VideoDataset(): def __init__(self, directory, fraction=0.2, sub_sample_rate=2): print("Initializing VD") = directory self.fraction = fraction self.sub_sample_rate = sub_sample_rate all_files = [os.path.join(self.directory, file) for file in os.listdir(self.directory)] valid_files = [] for file in all_files: try: # Read the serialized tensor from file serialized_tensor = tf.io.read_file(file) # Deserialize the tensor tensor = tf.io.parse_tensor(serialized_tensor, out_type=tf.float32) # Adjust dtype if necessary # Validate the shape of the tensor if tensor.shape == (16, 256, 256, 3): valid_files.append(file) except Exception as e: print(f"Error loading file {file}: {e}") # Randomly select a fraction of the valid files selected_file_count = int(len(valid_files) * fraction) print(f"Selected {selected_file_count} files") self.files = random.sample(valid_files, selected_file_count) def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 begin = [offset, 0, 0, 0] # start from index 'offset' in the frame dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3]] strides = [frame, 1, 1, 1] # step 'frame' in the Frame dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[0]) // frame #print(f"VD Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[0]}") if x.shape[0] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[0]}") return x def pooling(self, x, ksize): if ksize == 1: return x T, H, W, C = x.shape Hd = H // ksize Wd = W // ksize # Reshape the tensor to merge the spatial dimensions into the pooling blocks x_reshaped = tf.reshape(x, (T, Hd, ksize, Wd, ksize, C)) # Take the mean across the dimensions 3 and 5, which are the spatial dimensions within each block pooled_x = tf.reduce_mean(x_reshaped, axis=[2, 4]) return pooled_x def __len__(self): return len(self.files) def __getitem__(self, idx): #print("Calling VD getitem method") serialized_tensor = tf.io.read_file(self.files[idx]) video_tensor = tf.io.parse_tensor(serialized_tensor, out_type=tf.float32) x1 = video_tensor x2 = self.sub_sample(x1) x3 = self.sub_sample(x2) x4 = self.sub_sample(x3) #print("\n") x1 = self.pooling(x1, 8) x2 = self.pooling(x2, 4) x3 = self.pooling(x3, 2) #print(f"Shapes of VD output = {x1.shape}, {x2.shape}, {x3.shape}, {x4.shape}") return (x1, x2, x3, x4) def __iter__(self): print(f"Calling VD iter method, len self = {len(self)}") #Make the dataset iterable, allowing it to be used directly with tf.data.Dataset.from_generator. for idx in range(len(self)): yield self[idx]self.directory 

Traceback (most recent call last): File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/ops/script_ops.py", line 270, in __call__ ret = func(*args) ^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/data/ops/from_generator_op.py", line 198, in generator_py_func values = next(generator_state.get_iterator(iterator_id)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/3TF-TGANv2.py", line 140, in __iter__ yield self[idx] ~~~~^^^^^ File "/workspace/3TF-TGANv2.py", line 126, in __getitem__ x2 = self.sub_sample(x1) ^^^^^^^^^^^^^^^^^^^ File "/workspace/3TF-TGANv2.py", line 99, in sub_sample x = tf.strided_slice(x, begin, end, strides) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler raise e.with_traceback(filtered_tb) from None File "/root/anaconda3/envs/tf_gpu/lib/python3.11/site-packages/tensorflow/python/eageexecute.py", line 59, in quick_execute except TypeError as e: tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin and size arguments to be 1-D tensors of size 2, but got shapes [4] and [2] instead. [Op:StridedSlice] 

 def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 begin = [offset, 0, 0, 0] # start from index 'offset' in the frame dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3]] strides = [frame, 1, 1, 1] # step 'frame' in the Frame dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[0]) // frame #print(f"VD Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[0]}") if x.shape[0] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[0]}") return x 

 def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 # begin = [0, offset, 0, 0, 0] # start from index 'offset' in the second dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3], original_shape[4]] strides = [1, frame, 1, 1, 1] # step 'frame' in the second dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[1]) // frame #print(f"Gen Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[1]}") if x.shape[1] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[1]}") return x 

 #Create dataloader dataset = VideoDataset(directory) dataloader = tf.data.Dataset.from_generator( lambda: iter(dataset), # Corrected to use iter() to clearly return an iterator from the dataset output_signature=( tf.TensorSpec(shape=(16, 32, 32, 3), dtype=tf.float32), tf.TensorSpec(shape=(8, 64, 64, 3), dtype=tf.float32), tf.TensorSpec(shape=(4, 128, 128, 3), dtype=tf.float32), tf.TensorSpec(shape=(2, 256, 256, 3), dtype=tf.float32) ) ).batch(batch_size) 

class VideoDataset(): def __init__(self, directory, fraction=0.2, sub_sample_rate=2): print("Initializing VD") self.directory = directory self.fraction = fraction self.sub_sample_rate = sub_sample_rate all_files = [os.path.join(self.directory, file) for file in os.listdir(self.directory)] valid_files = [] for file in all_files: try: # Read the serialized tensor from file serialized_tensor = tf.io.read_file(file) # Deserialize the tensor tensor = tf.io.parse_tensor(serialized_tensor, out_type=tf.float32) # Adjust dtype if necessary # Validate the shape of the tensor if tensor.shape == (16, 256, 256, 3): valid_files.append(file) except Exception as e: print(f"Error loading file {file}: {e}") # Randomly select a fraction of the valid files selected_file_count = int(len(valid_files) * fraction) print(f"Selected {selected_file_count} files") self.files = random.sample(valid_files, selected_file_count) def sub_sample(self, x, frame=2): original_shape = x.shape # Logging original shape offset = 0 begin = [offset, 0, 0, 0] # start from index 'offset' in the frame dimension end = [original_shape[0], original_shape[1], original_shape[2], original_shape[3]] strides = [frame, 1, 1, 1] # step 'frame' in the Frame dimension x = tf.strided_slice(x, begin, end, strides) expected_frames = (original_shape[0]) // frame #print(f"VD Expected frames after sub-sampling: {expected_frames}, Actual frames: {x.shape[0]}") if x.shape[0] != expected_frames: raise ValueError(f"Expected frames: {expected_frames}, but got {x.shape[0]}") return x def pooling(self, x, ksize): if ksize == 1: return x T, H, W, C = x.shape Hd = H // ksize Wd = W // ksize # Reshape the tensor to merge the spatial dimensions into the pooling blocks x_reshaped = tf.reshape(x, (T, Hd, ksize, Wd, ksize, C)) # Take the mean across the dimensions 3 and 5, which are the spatial dimensions within each block pooled_x = tf.reduce_mean(x_reshaped, axis=[2, 4]) return pooled_x def __len__(self): return len(self.files) def __getitem__(self, idx): #print("Calling VD getitem method") serialized_tensor = tf.io.read_file(self.files[idx]) video_tensor = tf.io.parse_tensor(serialized_tensor, out_type=tf.float32) x1 = video_tensor x2 = self.sub_sample(x1) x3 = self.sub_sample(x2) x4 = self.sub_sample(x3) #print("\n") x1 = self.pooling(x1, 8) x2 = self.pooling(x2, 4) x3 = self.pooling(x3, 2) #print(f"Shapes of VD output = {x1.shape}, {x2.shape}, {x3.shape}, {x4.shape}") return (x1, x2, x3, x4) def __iter__(self): print(f"Calling VD iter method, len self = {len(self)}") #Make the dataset iterable, allowing it to be used directly with tf.data.Dataset.from_generator. for idx in range(len(self)): yield self[idx] 

First of all, these are the contact details to reach us for help any type of academic task of any subject:
MY CONTACT INFO:
WhatsApp: +1 (213) 594-5657
Call: +1 727 456 9641
Website: hiraedu. com
Email: info@hiraedu. com
ASSESSMENTS I CAN COMPLETE:

• Online Courses (1 - 20 Weeks Long)
• Homework Assignments
• Exams Quizzes Tests
• Excel Projects
• Discussion Forum Posts
• Research Papers Term Papers Essays
• Employment Assessments

MY MATH SUBJECTS OF EXPERTISE:
I am very knowledgeable and proficient in assisting students in a wide range of mathematics classes. I can help students complete their homework assignments and other projects get an A on quizzes, tests, and exams (including proctored assessments) answer online discussion posts write essays & papers in MLA APA Chicago format and provide general overall academic help in each math course listed below:

• Advanced Placement Honors High School Level Math
• AP Calculus AB A.P. Calculus BC
• AP Statistics
• Biostatistics Bio-Statistics
• College Algebra Intermediate Algebra
• Business Calculus
• Business Statistics
• Business Math
• Calculus I Calculus II Calculus III (Calc)
• Geometry
• International Baccalaureate (IB) Maths
• Liberal Arts Math
• Logic & Reasoning
• Multivariable Calculus Multi variable Calculus
• Precalculus Pre-calculus
• Probability & Statistics
• Psychology Statistics Psych Stats
• Quantitative Reasoning Quantitative Methods Quantitative Literacy (Quant)
• Statistics (Stats)
• Statistics for Social Sciences
• Trigonometry (Trig)

STATISTICS HELP (MY BEST SUBJECT):

• I have a master's degree in Applied Statistics from a university in New Jersey, USA. As a result, I am very proficient in helping students with various types of Statistics courses including Biostatistics Business Statistics Elementary Statistics Probability & Statistics Psychology Statistics.
• I have extensive knowledge and skill with helping students get through various statistical concepts including all of the following in alphabetical order:
• ANOVA Bar Graph, Boxplot, Dot Plot, Histogram, Pie Chart Bias Binomial, Exponential, Poisson, Gamma, Uniform Distribution Central Limit Theorem Chi-Square Test Coefficient of Determination (R^2) Conditional & Relative Probability Confidence Interval Correlation Coefficient (r) Critical Region Critical Value Descriptive & Inferential Statistics Effect Size Expected Value Experiment vs Observation Goodness of Fit Test Hypothesis Testing Interpolation & Extrapolation Interquartile Range (IQR) Kurtosis Left, Negative, Right, Positive Skewed Distribution Logistic Regression Mean, Median, Mode, Midrange Multiple Regression Nominal, Ordinal, Interval, Ratio I Population Parameter Normal, Symmetric & Bell Shaped Distribution Null & Alternate Hypothesis One & Two Sample T-Test One & Two Sample Z-Test Ordinary Least Squares Outliers P-Value Pooled Variance (Equal Variances) Proportion R^2 Range Regression Regression Line Reject & Fail to Reject Ho Relative & Cumulative Frequency Distributions Relativity & Reliability Right, Left, One, Two Tailed Tailed Hypothesis Test Sample Statistics Sampling Methods (Convenience, Stratified, Simple, Cluster, Systematic) Significance Level (1%, 5%, 10%) Skewness Standard Deviation& Variance T-Score Test Statistic Time Series Type I & Type II Errors Unbiased Estimators Z-Score

ALGEBRA HELP:

• I am well-versed in helping students do well in Algebra 1 & 2 College Algebra Intermediate Algebra Pre-Algebra and I am very knowledgeable and proficient in the following algebra topics in alphabetical order:
• Absolute Values Circle Equation Domain Exponents Graphing Horizontal & Vertical Asymptotes Inequalities, Equations & Functions Inverse Functions Linear, Exponential, Logarithmic, Quadratic Equations Logarithms Maximum & Minimum Values of a Graph Number Lines One-to-One Functions Parallel & Perpendicular Lines Plotting Points & Ordered Pairs Point-Slope Form Polynomial Division Polynomials Radicals Range Rational & Irrational Expressions Rational Functions Real Roots Real Zeros Relations & Functions Slope Slope-Intercept Form Square Roots Standard Form of a Line System of Equations System of Inequalities Variables X and Y Intercepts

CALCULUS HELP:

• I am well-versed in helping students do well in Calculus I, II & III, Calculus 1, 2 & 3, Business Calculus, Multivariable Calculus, Vector Calculus and I am very knowledgeable and proficient in the following calculus topics in alphabetical order:
• Absolute & Local Maximum and Minimum Antiderivative Area & Volume Chain Rule Concavity Continuity Convergence Test Critical Points Curvature Curve of Rotation Curve Sketching Derivatives Divergence Test First & Second Derivative Test Fourier Series Implicit Differentiation Increasing / Decreasing Functions Infinite Series Integrals Integration by Parts Jacobian Matrix Limits L’Hopital’s Rule Maclaurin Series Mean Value Theorem Partial Derivative Product & Quotient Rule Reiman Sum Related Rates Simpson’s Rule Solid of Revolution Substitution Rule Taylor Series

ATTRIBUTES THAT SET ME APART FROM OTHER TUTORS:

• I firmly believe that I have many attributes and skills that set me apart from other tutors. Below is a list of qualities that makes me an ideal fit to help you master your coursework:

I CAN AID STUDENTS TAKING PROCTORED ASSESSMENTS:

• Proficient in CollegeBoard AP Calculus AB, AP Calculus BC & AP Statistics exams.
• Skilled with International Baccalaureate Math Exams: IB Maths HL & IB Maths SL
• Can intentionally answer a few problems incorrectly at the student's request.
• Can text solutions to the student while they are taking a proctored exam.
• Upon request, can purposely obtain grades below an A (ex: B+, B, C+, C-).
• I can stretch out the time to a plausible length for timed exams and assignments before submitting them.
• Have reliable software that can change my IP address to match the closest major city near the student.
• I have exceptional multiple-choice exam-taking skills. I have mastered the various techniques to make calculated guesses for the correct option in standardized exams such as the SAT & GRE.

I CAN VERIFY MY ACADEMIC KNOWLEDGE & SKILLS:

• I have no problem with requests to solve sample problems upfront to prove my knowledge & proficiency of the course concepts.
• I can provide glowing references & strong examples of work done for past clients to verify my academic skills.
• I can also provide, upon request, complete detailed calculations with explanations for solutions to the problems worked on.
• I am quite skilled and adept in using multiple modernized educational platforms and software, including but not limited to: Brightspace MyStatLab D2L WebAssign Canvas MyMathLab Blackboard ALEKS
• Can format term papers and discussion posts in APA MLA Harvard Chicago Vancouver OSCOLA IEEE format.

I HAVE PAID ACCESS TO OVER 15 STUDY-HELP WEBSITES AND MATHEMATICAL SOFTWARE:

• I have multiple paid subscriptions to a wide range of academic tutoring resources, including Chegg, Quizlet, CourseHero, Brainly, Grammarly, Study. com, Bartleby, Slader, Transtutors, Slideshare, StudocU, Turnitin, Academia, SymboLab, WolframAlpha, and Scribd to help guide to solve exam & homework questions that I may not immediately know.
• However, I am smart enough not to blatantly plagiarize solutions online but instead write them in my own unique words, formatting, & mathematical terminology.
• I have access to a very sophisticated graphing calculator and various mathematical software that provides step-by-step solutions to complex mathematical problems within seconds, allowing me to provide exact solutions to the student in a timely manner.
• I have impeccable handwriting and a high-quality mobile scanner app that allows me to scan written solutions in very legible high-definition JPG, PNG, or PDF formats.

I ALWAYS ACCEPT CALLS:

• I have no problem speaking with potential clients on the phone.
• I have an authentic suburban upper-middle-class American accent, similar to the actor: Ben Stein.
• However, be wary of so-called “tutors” who refuse to accept any phone calls or have a strict “text / email only” policy.
• These “text-only” / “email-only” tutors are usually located outside the U.S. and are afraid their foreign accent will expose their true nationality.

I WRITE LIKE A PROFESSIONAL:

• Approximately 98% of the time, I write all my texts & emails in grammatically correct U.S. English with proper punctuation and occasional typo.
• Unlike some alleged “U.S.-based tutors,” I do NOT write using broken English or grammatically incorrect texting shorthand (example: "wut u need help wit??").
• I write my ad in proper American English, the same way I always text and email people.
• I can guide students on formatting & writing term papers, essays, and discussion posts in APA MLA Chicago Harvard format.
• If the tutor you’re checking out consistently writes using egregiously horrendous grammar, do you honestly think it’s a good idea to trust them to help you do well in your class?

MY EDUCATIONAL SOFTWARE OF EXPERTISE:

• I am very knowledgeable and well-versed in a wide range of educational software to tutor students in including but not limited to: Acellus ALEKS APEX Learning Aplia Blackboard Brightspace Canvas Cengage CengageNow Connect ConnectMath Connexus CPM D2L Discord Edmentum Eviews Examity Excel Hawkes Learning Honorlock Khan Academy Knewton LimeSpring Lumen Maple MasteringChemistry MasteringPhysics MathXL (XL) MATLAB McGraw-Hill Microsoft Word Minitab Moodle MyAccountingLab MyEconLab MyITLab MyLab My Lab MyMathLab My Math Lab MyOpenMath MyPsychLab MySocLab MyStatLab My Stat Lab NCSS Open Source Software Pearson PERT PlatoWeb PowerPoint Prezi R Respondus Lockdown Browser with Webcam Sapling SPSS Stata StraighterLine Straighter Line WebAssign Web Assign Webex (Cisco) WebWork Wiley WileyPlus Wonderlic Zoom

SCHOOLS FROM WHICH I'VE HELPED STUDENTS IN :
As of 2021, I have tutored and helped students enrolled at the following U.S. universities community colleges county & city colleges schools for-profit institutions listed below in alphabetical order:

• A - C: American Public University System (APUS) Arizona State Ashford Brigham Young Brown Broward California State (Fullerton) Capella Chamberlain (Illinois) Colorado Technical Columbia Columbia Southern Cornell
• D - K: Dallas College Dartmouth DeVry Excelsior College Florida International Florida State Full Sail Georgia State Grand Canyon Harvard Houston Community College Ivy Tech Community College Kaplan Kennesaw State Kent State
• L - T: Liberty Lone Star College System Kaplan Miami Dade Michigan State CUNY State University of New York (SUNY) Northern Virginia Ohio State University of Phoenix (UOP) Penn State Princeton Purdue (Davenport) San Diego State San Francisco State San Jose State Southern New Hampshire Southern Nevada Stanford St. Petersburg Strayer Texas Tech
• UNIVERSITY OF: Alabama Arizona California (UC) Central Florida Chicago Cincinnati Florida Houston Illinois (Urbana-Champaign) Iowa Maryland Minnesota Michigan North Texas Pennsylvania Southern Florida Texas Toronto
• U - Z: UCLA Extension Utah State Utah Valley Valencia Walden U Wake Technical Western Governors Wilmington Yale

I OFFER FLEXIBLE PAYMENT PLANS:

• I offer several unique payment plans, including a weekly plan, a discounted monthly plan, a discounted half-now / half-later plan, and highly discounted up-front rates.
• I can accept the following payment methods for my services: PayPal Debit Cards Square Cash Credit Cards Cash App Venmo.
• Only people located in North America have access to Venmo Cash App Zelle.
• This is an excellent way to determine if the person you're dealing with is from the United States.
• Within reason, I offer a partial or full money-Back guarantee if the student is very unsatisfied with my academic services.

HELP AVAILABLE FOR OTHER SUBJECTS:

• For other subjects, you can reach out to me, and I can connect you with one of my tutoring partners who can provide general academic assistance for the following subjects:
• Art & Design: Art History Design Film Studies
• Business: Accounting (Acc) Biz Courses Business Administration (Biz Admn) Business Calculus Business Law Business Statistics Corporate Finance Economics (Econ) Finance (Fin) Financial Accounting Intermediate Accounting Intermediate Microeconomics (Micro) Macroeconomics (Macro) Management (Mgmt) Managerial Accounting Marketing MBA
• Computer Science: C C++ Computer Programming (Comp) Java Python R (Introduction & Intermediate Level Only)
• Humanities: Communications (Comm) English Literature Essay & Term Paper Writing, Editing, Proofreading Essays History Papers Philosophy (Phil) Religious Studies Theology (Theo)
• Mathematics: A.P. Calculus AB & BC Psychology Statistics AP Statistics Bio statistics Calculus I (1) Calculus II (2) Calculus III (3) College Algebra Differential Equations Discrete Math Finite Math Functions General Math Geometry Liberal Arts Math Linear Algebra Multi-variable Calculus Partial Differential Equations Precalculus Probability Quantitative Reasoning Real Analysis Statistics Trigonometry (Trig) Vector Calculus
• Science & Medicine: Anatomy & Physiology (A & P) Biochemistry Biology (Bio) Biostatistics Earth Science Environmental Science General Chemistry (Chem) Healthcare Medicine Nursing Nutrition Organic Chemistry Physics (Phys)
• Social Sciences: Anthropology Economics (Econ) Geology Law & Justice Liberal Arts Liberal Studies Political Science Psychology Sociology

THE OBLIGATORY "IS THIS A SCAM?" QUESTION:
Considering the fact that you found my contact information online, it’s understandable to be skeptical regarding the legitimacy of my services. Therefore, I’m willing to do all of the following to help you feel more secure in trusting me with your academic needs:

• 1) Speak with you on the phone and answer any questions you may have so that you have the opportunity to assess and analyze my tone, articulation, accent, diction, dialect, regionalisms, pitch, vocabulary, word choice, intonations, and inflections.
• NOTE: I’m a New Jersey-born Irish/Italian-American, but I have been told that I speak in an erudite & articulate suburban American accent with a “professor style” voice somewhat resembling the voice of actor: Ben Stein.
• 2) Agree to do some homework questions of yours upfront with the work fully shown in real-time to prove my mastery of the course content.
• 3) Send you screenshots of at least five past scores I attained from completing exams & homework assignments for other students in the same type of class you need help with.
• 4) Send you full screenshots of written exam solutions I wrote up by hand for students in the past in the same course that you need help with so that you can see my neat handwriting.
• 5) Provide you with the contact information of students who have utilized my services in the past who would be more than willing to vouch for my skills.

HOW TO CONTACT ME:

• If you are interested in my services and would like to learn more info, please feel free to reach out to me.
• Text messages and email are the fastest and easiest ways to get in touch with me. However, I gladly accept phone calls.
• If I don't respond right away, I am most likely helping a student at the time and not currently available to speak on the phone. But I always make a solid effort to reply to texts, emails, and phone calls within an hour.

CONCLUSION:

• I take my work as a tutor very seriously, and I always aim to do my best to help my students excel in school. I have a solid and sincere passion for academic achievement, and with my help, I am positive that I can help students obtain very high grades with minimal stress and effort.
• Please feel free to contact me if you're interested in my services and would like to learn more. I'm available 24/7 via phone call, email, and text. Again, thank you for taking the time to read my Reddit tutoring ad, and I hope to hear from you soon!

OCT 2021 UPDATE: I am currently offering discount deals for requests for assistance with completing a student's entire course for the Fall 2024 semester (14 - 20 week courses acceptable), as well as discounts for students seeking help with multiple exams and/or multiple classes for Fall 2024. My availability for the Autumn 2024 / Fall 2024 semester will likely become limited very quickly as I receive more and more academic requests. Therefore it would be very advantageous to reach out to me for academic assistance before my schedule becomes too full.
MY CONTACT INFO:
WhatsApp: +1 (213) 594-5657
Call: +1 727 456 9641
Website: hiraedu. com
Email: info@hiraedu. com
IMPORTANT: When reaching out, please try to include the following information in the initial text message or email so that I can have all the important details necessary to determine the rate for my services:

• Name You Prefer to Be Called
• Name of the Class You Need Help With
• Type of Assessment Help Needed: (Entire Course, Exams Only, HW Assignment, Essay, etc.)

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There have been lots of interesting social media posts lately making use of the fact that the number of ways a deck of 52 cards can be ordered is astronomically large. Specifically, 52! ways, or 8e67 in scientific notation. It's therefore mathematically impossible that more than a tiny fraction of these possible orders have been actually shuffled since the beginning of time.
These posts take it a step farther, making examples of the number of times you'd have to shuffle a deck before you'd be likely to get an identical ordering. Most examples seem to shoot for 52!/2, or 4e67 shuffles before it's more likely than not that you'd find an identical ordering. I believe these estimates are incorrect, and I'm going to use the classic "birthday problem" to illustrate why.
The birthday problem imagines an empty room, with people walking in one by one. As the population of the room incrementally increases, the problem asks this "at what point is it more likely than not that 2 people in the room share a birthday?" The common, intuitive, and also incorrect answer is 365/2 people, or 183 whole human beings. However, this is answering a different question. 183 people is the point at which it's more likely than not that someone shares a birthday with the FIRST person in the room. What was actually asked was the odds that ANY two people share a birthday. We need to take into account all the possible pairings of people in the room. When we do that, we find that the answer is 23. There are 253 ways to pair up 23 people, far more than half the number of days in a year. This surprisingly low answer is known as the birthday paradox.
Taking it back to the card problem, we can see that 52!/2 is actually the point at which it becomes likely that you'll get a duplicate of shuffle #1. But we're looking for the point that it's likely that ANY two shuffles are identical. It follows then that point must be much, much sooner than 52!/2. Still likely an enormous number, but much smaller than commonly stated. But how would we calculate it? We could try to use the same formula commonly used to solve the birthday problem: find the odds of a unique shuffle for N number of shuffles.
The odds of shuffle 1 being unique: 1, or course.
The odds of shuffle 2 being unique: (52!-1)/52!
The odds of shuffle 3 being unique: (52!-2)/52!
The odds of shuffle 4 being unique: (52!-3)/52!
To get the odds that all of these four shuffles are unique, we need to multiply. So, the probability of all four shuffles being unique is: (52!-1)(52!-2)(52!-3)(52!-4)/52!^4.
Taking it to an arbitrary n number of shuffles, we could calculate the odds like this: (52!-1)(52!-2)...(52!-(n-1))/52!^(n-1)
What we want to know is, at what n do the odds of all shuffles being unique drop below .5?
You can see that calculating numbers this big becomes totally unworkable very quickly. What we need is a way to come up with a good estimate. That's where the Birthday Attack comes in.
The Birthday Attack is a cryptographic attack that tries to find collisions in hash functions. For a hash function f(x)=H, with H being the number of all possible function outputs, how many random inputs would we have to put in until it's more likely than not that we get a duplicate H? The Birthday Attack has an answer: roughly 1.25*sqrt(H).
If we take f(x) to be our card shuffling function, which is already random by nature, H would be the total number of possible ways a 52 card deck can be ordered, which we already know to be 52!. So, we can estimate the point at which a duplicate shuffle becomes more likely than not as 1.25*sqrt(52!), or 1.12e34. Still a huge number, but many factors smaller than the commonly stated 4e67!
If you read this far, thank you for entertaining my random mathematical musings! Below are the links to the wiki entries for the Birthday Problem and the Birthday Attack, which I find incredibly interesting.
https://en.wikipedia.org/wiki/Birthday_problem
https://en.wikipedia.org/wiki/Birthday_attack

Hello, I have a few clarifications when doing this. We have a research due in two days and have had zero lessons about what we have to do to complete a quanti research.

1. When following the seven steps, we refer to the table of critical values of r for the critical values, right?
   
2. When we compare the calculated correlation coefficient with the crit value, what does this mean? Is it the value we get from solving Pearson's r? The one where you need the summation of x, y, xy, x², and y²?
   
3. If so, where does the t-value come into place here? The one where you need the standard dev of both variables and the mean of both. I've seen some solve it for a problem needing pearson. Does it even have something to do with it? Because I think the tvalue is only for the ttest?
   

I'm very sorry if I sound too dumb right now but we have been taught nothing so far. All the things I know rn are just coming from me trying to connect all these tiny bits of information given by the little learning materials shared to us.

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Why is 5^-1 equal to 1/5
I know how I calculate with negative exponents, but what is the science behind this law of:
x^-a=1/xa
I know that the 1 is there, because of 5^0=1, so 5^-1=1/5
Also 3*1=1+1+1
But 0,9*0,9=??
How can i display a under-one number multiplication as a addition or just how can i display it?
I know its 0,81 because I just calculate 9*9 and put the comma in the right place

I have a little confusion in choosing test
1.There are 3 groups.Normal People, People with Non - Blue light , People with Blue Light.
My Alternative Hypothesis is There is significant difference between these 3 groups.
So I decided to choose ANOVA post doc in g power to calculate sample size.
Because I thought it's two tailed test.
And I know the procedure to do ANOVA post doc
2.There are 3 groups.Aged with No disease ,(1,2,3) Early AMD , AMD.
My Alternative Hypothesis is Atleast one group differs significantly from the overall mean of the dependent variable. (Researcher trying to prove AMD is increasing than other group.So I thought this hypothesis is best)
I decided to do prior analysis in g power.I thought it's one tailed.
Am I correct with hypothesis and test?
If it's wrong, someone pls correct it .

I have a little confusion in choosing test
1.There are 3 groups.Normal People, People with Non - Blue light , People with Blue Light.
My Alternative Hypothesis is There is significant difference between these 3 groups.
So I decided to choose ANOVA post doc in g power to calculate sample size.
Because I thought it's two tailed test.
And I know the procedure to do ANOVA post doc
2.There are 3 groups.Aged with No disease ,(1,2,3) Early AMD , AMD.
My Alternative Hypothesis is Atleast one group differs significantly from the overall mean of the dependent variable. (Researcher trying to prove AMD is increasing than other group.So I thought this hypothesis is best)
I decided to do prior analysis in g power.I thought it's one tailed.
Am I correct with hypothesis and test?
If it's wrong, someone pls correct it .

Hey Everyone,
Passed the exam with AT/AT/AT earlier this week. I got a lot of useful information out of this sub so hopefully, some of these tools. tips, and tricks can help someone going down the PMP path. :)
Taking The Exam
So as you know, the test is 4 hours long 180 questions. For pure test taking suggestions I would recommend the below.

• Do it in person, if possible.
  • The test centers are quiet, nice, and in my experience, friendly.
  • Doing it online, while flexible, leaves many variables open that doing it in person will solve. Most importantly, you're not subject to a proctor you can't see and talk to in regards to whatever concerns they/you may have.
• Take your breaks
  • You get two break, use them. It's an important way to help you rest your brain for a bit and move around. It is extremely hard to focus on 180 questions at one time over several hours.
• Show up early to the testing center
  • At least at my testing center, it was really busy. Show up early so you're among the first to check in and get going. Helps prevent anxiety as well.

Test Itself

• About 5 drag and drop questions
• About 5 multiple selection questions
• 1 graph question
• 2 calculation questions where you have to break out the calculator
  • No formula questions
• About 50% agile & 50% Predictive/Hybrid
• 2 questions I wasn't super confident in / didn't know
  • Some very specific agile questions that probably would be "Expert" in StudyHall
• Completed in 3 hr 30 min
  • Spent about an hour on each set of 60 questions
  • After completing a set I'd spend ~10-15 min reviewing my answers. Only changed a few of them after re-reading the questions.

Test Observations

• Lots of conflict resolution questions .
• Could be me but, things seemed to get progressively harder each set of 60 questions. Nothing unsolvable or things you weren't able to reduce.
• Lot's of questions to see if you'd follow the process.
• A few questions to see if you'd follow the law / regulations.
• Found that highlighting everything burned too much time. Ended up only highlighting questions that were long or complex.
• The strikethrough tool is your friend. Use it to eliminate bad options. I found that I was able to knock out at least 2 options almost instantly.
• Test tries to steer you towards escalating the issue to sponsoleadership. In very very rare cases, at least in the exam, should you escalate.
• Lots of questions on change management.
• Large emphasis on communication and stakeholder management.
  • Communicate, communicate, and communicate.
• StudyHall more or less directly aligns with the exam.

Exam Question Tips

• Always assess and then look for opportunities resolve
  • Resolve issues collaboratively with the team, if possible
• Always communicate and be an active listener. Confirm that your message is received and understood.
• Solve issues early and don't let them escalate.
• Prioritize legal issues and concerns. Can’t generate business value if you can’t get your stuff to market.
• Motivate, facilitate, and be a leader for your team.
• Train and mentor team members.
  • Enable mentoring, training, or shadowing for team members needing help with quality, developing experience, or direction.
• Ensure requirements are clear and if stakeholders don’t know what they want, help facilitate and guide them to a project vision and what they want out of it.
• Understand how to run a sprint (planning, stand ups, review, retrospectives, etc).
• Prioritize the backlog to deliver business value.
• Identify risks (both positive and negative) and put them in a risk register, identify risk responses.
• Include all stakeholders and ensure they receive communications.
• Always follow the plan, if you need to change the plan, conduct change management process
• Follow the contract and be a good business partner.
• Be conscientious of cultural differences and practices.
• Resolve conflicts collaboratively.

Materials Used
Andrew Raymadal 35 Hr. PMP Exam Prep Course (Udemy)

• Took notes on all of the sections, reviewed notes after completion
• Ok for covering the major sections but polishing was done elsewhere
• PM Mindset Tips were good
• 7/10: Good for foundation but long (nothing he can do about that) and arduous. Real understanding starts after the foundation has been completed.

PMI StudyHall

• Most important tool used for my preparation. Accurately reflects what the actual exam looks like
• Bought the basic package, if I were to do this again I would buy the more expensive package and focus on that package’s expanded set of questions and practice exams
• 9.75/10

PM Aspirant Process Group Game

• Good for mapping out predictive processes and how things interconnect
• Quick and easy to digest
• Full transparency: Norton Web Protect says it's a suspicious link. I didn't have any issues with it and didn't seem to find anything that would trigger that. Up to you if you're alright with using the tool.
• 8/10

PMP Exam-PMI New Format 2024 Mock Simulator (PMBOK7 Updated)

• Seemed like 80% agile
• Useful to the extent that it exposes you to agile questions but not really reflective of the actual exam
• Really good explanations though for the questions and answers
• 8/10

150 PMBOK 7 Scenario Based PMP Exam Questions and Answer

• Awesome set of questions and answers that will closely reflect what you will see on the exam
• To be transparent, I only went through about 25% of the video since I primarily used StudyHall
• 9.5/10

The Complete Project Management Body of Knowledge in One Video (PMBOK 7th Edition)

• Covers the PMBOK in a digestible manner
• Watched it 1 time and felt that that was all I needed for this overview
• 9.5/10

Materials Aware Of But Didn't Use
Third3Rock Notes

• Apparently really comprehensive and good but didn’t want to review/pay for any more materials

200 AGILE PMP Questions and Answers - the BEST Preparation for the Exam!

• Made by the guy who made the 150 PMBOK 7 Scenario PMP question
• From what I’ve read in the subreddit, also a great tool

100 WATERFALL PMP Questions and Answers - EXCELLENT Preparation for the Exam!

• Same as above

Conclusion
Hopefully some of these tips/resources are able to help you out. Keep grinding and putting in those long hours. It will be worth it. Good luck!

So, as the title suggests, I want to build a 'professional' line follower robot, or one good enough to compete in international competitions. I already have a basic/intermediate knowledge of electronics and robots of this type. I have participated in line follower competitions and even reached the podium. However, I believe the way I've been coding these robots' software is what's been holding them back from being truly great. I usually use a relatively simple PID algorithm that calculates how far from the center of the line the robot is and uses this value to calculate a differential and adjust each wheel's speed.
My question is: How can I improve this? What concepts do I need to learn to program a line follower robot in a truly professional way?
Mostly, I use an arduino nano, a motor driver drv8833, a sensor array with 8 or 16 sensors, and all the basic components you would expect in a line follower
Example of PID code I'm using right now (variables names are in portuguese)
This first function gets the position of the line
int leitura(void) {
for(int i = 6; i>=0; i--) {
sensores[i] = (analogRead(A0 + i));
if(linha==0){
if(sensores[i]<=limiar[i]){digital[i]=0;}else{digital[i]=1;}}
if(linha==1){if(sensores[i]<=limiar[i]){digital[i]=1;}else{digital[i]=0;}}
Serial.print(digital[i]);
Serial.print("\t");
}
somap = (600*digital[0]) + (500*digital[1]) + (400*digital[2]) + (300*digital[3]) + (200*digital[4]) + (100*digital[5]) + (0 * digital[6]);
soma = (digital[0] + digital[1] + digital[2] + digital[3] + digital[4] + digital[5] + digital[6]);
pos = (somap / soma);
if(lastPos <= 100 && pos == -1) {
pos = 0;
}
if(lastPos >= 500 && pos == -1) {
pos == 600;
}
lastPos = pos;
return pos;
}
PID function makes adjustments to the motors speed
void PID() {
proporcional = pos - setpoint;
derivativo = proporcional - last_prop;
integral = erro1 + erro2 + erro3 + erro4 + erro5 + erro6;
last_prop = proporcional;
erro6 = erro5;
erro5 = erro4;
erro4 = erro3;
erro3 = erro2;
erro2 = erro1;
erro1 = proporcional;
int diferencial = (proporcional * KP) + (derivativo * KD) + (integral * KI);
if(diferencial > vel)diferencial = vel;
else if(diferencial < -vel) diferencial = -vel;
(diferencial < 0)?
motores(vel-diferencial, vel) : motores(vel-diferencial, vel);
}
I would really apreciate if someone helped me with this. Articles, youtube videos, etc, are really welcome.

I am building a Support Vector Regression model to predict the Run Time of Bus based on some of these features: GPS longtitude, latitude, and segment the bus is on.
I have 2 questions:

1. I am tryting to tune the hyperparameters in order to achieve the best results by using gridSearchCV to search for the best params in a param grid. Am i following the right approach and are there any better ways i can implement my model to get the best hyperparameters?
2. I try to run the code in both Kaggle and Google Collab but it runs for hours and finnaly cannot execute and timed out. Is this because that my dataset is too large? I have 130464 records in my dataset. Below is the code of my model. I really appreciate if you guys can take a look.

​

from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.svm import SVR from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.metrics import mean_squared_error # Separate features and target variable X = df[['segment_latitude', 'segment_longitude', 'segment']] y = df['segment_run_time'] # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create a pipeline with scaling and SVR pipeline = Pipeline([ ('scaler', StandardScaler()), ('svr', SVR(kernel='rbf', gamma='scale')) ]) # Define the parameter grid param_grid = { 'svr__C': [0.1, 1, 10], # Different values of C 'svr__epsilon': [0.1, 0.2, 0.5] # Different values of epsilon } # Perform grid search grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error') grid_search.fit(X_train, y_train) # Get the best parameters best_params = grid_search.best_params_ print("Best Parameters:", best_params) # Predict on testing set using the best estimator best_estimator = grid_search.best_estimator_ y_pred = best_estimator.predict(X_test) # Evaluate the model mse = mean_squared_error(y_test, y_pred) print("Mean Squared Error:", mse) # Calculate RMSE rmse = mean_squared_error(y_test, y_pred, squared=False) print("Root Mean Squared Error:", rmse) 

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• Flexibility remains the biggest advantage to monthly plans. Subscribers can pause service anytime without penalties.
• Monthly IPTV provides easy trial periods before committing further. Users test services risk-free to confirm compatibility.
• Short-term accessibility lets subscribers adapt packages as channel lineups or personal needs change monthly.
• Cancellation happens merely by non-renewal versus expensive annual contracts. Buyers dodge long-term obligations.

However, monthlies also carry certain drawbacks.

1-Month Cons

• Cost proves significantly higher over 12 months versus discounted annual packages. Monthly costs accumulate faster long-run.
• Manual renewal required every 30 days versus automated recurring annual billing simplifying payment process.
• Less incentive for providers to prioritize monthly users versus loyal annual subscribers in cases of server capacity issues.
• No buffer period exists to test services across variable conditions like changing locations before committing 12+ months.

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• Annual commitments receive price-lock protection against future inflation that monthlies lack. Rates remain stable.
• Ongoing access remains uninterrupted whenever internet or billing issues arise with automated recurring payments.
• Long-term users benefit from prioritization in user support and service reliability over monthlies.

However, lengthier contracts also involve some disadvantages.

12-Month Cons

• Less flexibility for adapting subscriptions if channel selections or personal needs change significantly after 6-12 months.
• Annual packages require larger upfront costs ($468 total) versus spreading payments monthly.
• Subscribers lose access until next renewal period if unable to afford payments temporarily due to financial hardship.
• Longer commitment hinders testing different providers within 12 months without financial penalties.

In truth, neither monthly nor annual subscriptions unequivocally dominate in all scenarios. Individual circumstances like household needs, financial factors, risk-tolerance and technical expertise influence the optimal term.
Premium IPTV Services recommends weighing both short and long-term benefits with one's own unique requirements to decide whether monthly flexibility or annual discounts suit best. Buyers choose the strategy maximizing value and satisfaction over their anticipated usage period.
Overall, annual commitments offer maximum discounts and stability suiting stable needs while monthlies preserve flexibility for variable or less-committed situations. No single answer prevails for every individual cord-cutter.

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• Flexibility remains the biggest advantage to monthly plans. Subscribers can pause service anytime without penalties.
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• Short-term accessibility lets subscribers adapt packages as channel lineups or personal needs change monthly.
• Cancellation happens merely by non-renewal versus expensive annual contracts. Buyers dodge long-term obligations.

However, monthlies also carry certain drawbacks.

1-Month Cons

• Cost proves significantly higher over 12 months versus discounted annual packages. Monthly costs accumulate faster long-run.
• Manual renewal required every 30 days versus automated recurring annual billing simplifying payment process.
• Less incentive for providers to prioritize monthly users versus loyal annual subscribers in cases of server capacity issues.
• No buffer period exists to test services across variable conditions like changing locations before committing 12+ months.

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• At only $39 per month, Premium IPTV Services 12-month packages save up to 45% on regular monthly rates. Buyers maxmise value.
• Annual commitments receive price-lock protection against future inflation that monthlies lack. Rates remain stable.
• Ongoing access remains uninterrupted whenever internet or billing issues arise with automated recurring payments.
• Long-term users benefit from prioritization in user support and service reliability over monthlies.

However, lengthier contracts also involve some disadvantages.

12-Month Cons

• Less flexibility for adapting subscriptions if channel selections or personal needs change significantly after 6-12 months.
• Annual packages require larger upfront costs ($468 total) versus spreading payments monthly.
• Subscribers lose access until next renewal period if unable to afford payments temporarily due to financial hardship.
• Longer commitment hinders testing different providers within 12 months without financial penalties.

In truth, neither monthly nor annual subscriptions unequivocally dominate in all scenarios. Individual circumstances like household needs, financial factors, risk-tolerance and technical expertise influence the optimal term.
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