I noticed over the next 6 turns or so that their armies got steadily weaker, because they had less resources. They agreed to peace on the third turn and became my vassal. In a blitz of their land, I took 6 of their provinces in two turns, only fighting the stack of ashigaru. Hattori had 7 provinces and 2 full stacks (mostly samurai) and one half stack, and one mostly full stack (of ashigaru). Recently on Legendary I was Ikko-Ikki against Hattori. So even with just their one island town (Sado), they can field quite a few units. Homna, for instance, has very rich province with gold mines. The reason some clans seem like they have more money is because they have richer provinces.  Nor can the AI make more units per turn than it has recruitment slots, just like the player. With the exception of the Ashikaga Shogunate and Rebels, you won't see the AI magically make units if it has no money. The AI still needs to purchase each unit it makes. The AI doesn't cheat beyond this, however. The AI also has lower upkeep costs for its units, so can generally field larger armies than the player can with less provinces. On Normal, it is only a slight advantage, and on Hard onwards the AI gets more bonuses relative to the player. On Very Easy and Easy, the player actually has an advantage. The way it works is that on easier difficulies, your own units get bonuses, whereas the higher difficulties the AI gets bonuses to their units' stats. There is a way to fix this without having to play on a easier difficulty.ĭiscussion update: I noticed that if you play your battles with another players (Drop in) they seem to keep the ♥♥♥♥ing units buffs How is this suppose to be a strategy game if everyone else around you cheats?. I won't even mention that some clans have economy boost and huge stacks and powerfull agents.   Originally posted by Yao Ming:I'm playing on hard difficulty, and I'm pretty sure the AI has some advantage i don't.įor example: I have Bow samurais (with improved accuracy) and the enemy army has Ashigaru.

Shogun 2 esf editor

The role of each shape feature in differentiating the leaves from one another is studied using region of convergence (ROC) curves. In order to evaluate the performance of the presented work six leaf classes were adopted and its shape features were extracted. This objective is achieved with the designing of a classification system using shape features to obtain high accuracy of end results. The objective of the present work is to design computer-aided classification (CAC) system to discriminate among various medicinal leaves. Electrical and Instrumentation Engineering Department, Thapar University, Patiala (Punjab), India Ĭlassification, medical leaf, feature extraction, NN classifiers, Otsu segmentation, FFBNN Abstract

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